{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FSRS4Anki Optimizer mini-batch_pls_offset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-spaced-repetition/fsrs4anki/blob/main/archive/experiment/mini-batch.ipynb)\n",
    "\n",
    "↑ Click the above button to open the optimizer on Google Colab.\n",
    "\n",
    "> If you can't see the button and are located in the Chinese Mainland, please use a proxy or VPN."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upload your **Anki Deck Package (.apkg)** file or **Anki Collection Package (.colpkg)** file on the `Left sidebar -> Files`, drag and drop your file in the current directory (not the `sample_data` directory). \n",
    "\n",
    "No need to include media. Need to include scheduling information. \n",
    "\n",
    "> If you use the latest version of Anki, please check the box `Support older Anki versions (slower/larger files)` when you export.\n",
    "\n",
    "You can export it via `File -> Export...` or `Ctrl + E` in the main window of Anki.\n",
    "\n",
    "Then replace the `filename` with yours in the next code cell. And set the `timezone` and `next_day_starts_at` which can be found in your preferences of Anki.\n",
    "\n",
    "After that, just run all (`Runtime -> Run all` or `Ctrl + F9`) and wait for minutes. You can see the optimal parameters in section **2.3 Result**. Copy them, replace the parameters in `fsrs4anki_scheduler.js`, and paste them into the custom scheduling of your deck options (require Anki version >= 2.1.55).\n",
    "\n",
    "**NOTE**: The default output is generated from my review logs. If you find the output is the same as mine, maybe your notebook hasn't run there.\n",
    "\n",
    "**Contribute to SRS Research**: If you want to share your data with me, please fill this form: https://forms.gle/KaojsBbhMCytaA7h8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here are some settings that you need to replace before running this optimizer.\n",
    "\n",
    "filename = \"../collection-2022-09-18@13-21-58.colpkg\"\n",
    "# If you upload deck file, replace it with your deck filename. E.g., ALL__Learning.apkg\n",
    "# If you upload collection file, replace it with your colpgk filename. E.g., collection-2022-09-18@13-21-58.colpkg\n",
    "\n",
    "# Replace it with your timezone. I'm in China, so I use Asia/Shanghai.\n",
    "# You can find your timezone here: https://gist.github.com/heyalexej/8bf688fd67d7199be4a1682b3eec7568\n",
    "timezone = 'Asia/Shanghai'\n",
    "\n",
    "# Replace it with your Anki's setting in Preferences -> Scheduling.\n",
    "next_day_starts_at = 4\n",
    "\n",
    "# Replace it if you don't want the optimizer to use the review logs before a specific date.\n",
    "revlog_start_date = \"2006-10-05\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Build dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Extract Anki collection & deck file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extract successfully!\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "import sqlite3\n",
    "import time\n",
    "from tqdm import notebook\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from datetime import timedelta, datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import torch\n",
    "from torch import nn\n",
    "import warnings\n",
    "\n",
    "notebook.tqdm.pandas()\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "\n",
    "\n",
    "# Extract the collection file or deck file to get the .anki21 database.\n",
    "with zipfile.ZipFile(f'./{filename}', 'r') as zip_ref:\n",
    "    zip_ref.extractall('./')\n",
    "    print(\"Extract successfully!\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Create time-series feature & analysis\n",
    "\n",
    "The following code cell will extract the review logs from your Anki collection and preprocess them to a trainset which is saved in [./revlog_history.tsv](./revlog_history.tsv).\n",
    "\n",
    "The time-series features are important in optimizing the model's parameters. For more detail, please see my paper: https://www.maimemo.com/paper/\n",
    "\n",
    "Then it will generate a concise analysis for your review logs. \n",
    "\n",
    "- The `r_history` is the history of ratings on each review. `3,3,3,1` means that you press `Good, Good, Good, Again`. It only contains the first rating for each card on the review date, i.e., when you press `Again` in review and  `Good` in relearning steps 10min later, only `Again` will be recorded.\n",
    "- The `avg_interval` is the actual average interval after you rate your cards as the `r_history`. It could be longer than the interval given by Anki's built-in scheduler because you reviewed some overdue cards.\n",
    "- The `avg_retention` is the average retention after you press as the `r_history`. `Again` counts as failed recall, and `Hard, Good and Easy` count as successful recall. Retention is the percentage of your successful recall.\n",
    "- The `stability` is the estimated memory state variable, which is an approximate interval that leads to 90% retention.\n",
    "- The `factor` is `stability / previous stability`.\n",
    "- The `group_cnt` is the number of review logs that have the same `r_history`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "revlog.csv saved.\n",
      "Trainset saved.\n",
      "Retention calculated.\n"
     ]
    },
    {
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       "model_id": "df6cf8a387554a159f2e6131e7cdd4f1",
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       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stability calculated.\n"
     ]
    },
    {
     "data": {
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       "model_id": "2a7dfb4f3a3641d2975786fedec54b0b",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "      r_history  avg_interval  avg_retention  stability  factor  group_cnt\n",
      "              1           1.7          0.765        1.0     inf       7997\n",
      "            1,3           3.9          0.876        4.2    4.20       4176\n",
      "          1,3,3           8.6          0.883        9.2    2.19       2711\n",
      "        1,3,3,3          18.2          0.858       14.0    1.52       1616\n",
      "      1,3,3,3,3          37.5          0.835       23.2    1.66        822\n",
      "    1,3,3,3,3,3          78.8          0.850       35.6    1.53        384\n",
      "  1,3,3,3,3,3,3         122.3          0.903       39.3    1.10        171\n",
      "              2           1.0          0.901        1.1     inf        240\n",
      "            2,3           3.5          0.946        8.3    7.55        201\n",
      "          2,3,3          11.1          0.890        7.1    0.86        160\n",
      "              3           1.5          0.962        5.4     inf       9134\n",
      "            3,3           3.9          0.966       15.2    2.81       6589\n",
      "          3,3,3           9.0          0.960       23.7    1.56       5162\n",
      "        3,3,3,3          19.4          0.942       44.2    1.86       3519\n",
      "      3,3,3,3,3          39.1          0.926       54.5    1.23       1922\n",
      "    3,3,3,3,3,3          76.6          0.930      106.8    1.96       1074\n",
      "  3,3,3,3,3,3,3         118.7          0.949      155.3    1.45        480\n",
      "3,3,3,3,3,3,3,3         131.1          0.970      617.1    3.97        100\n",
      "              4           3.8          0.966       12.1     inf      11599\n",
      "            4,3           8.1          0.975       38.9    3.21       7517\n",
      "          4,3,3          18.0          0.963       56.8    1.46       5303\n",
      "        4,3,3,3          33.3          0.952       84.3    1.48       3012\n",
      "      4,3,3,3,3          48.3          0.953      128.3    1.52       1353\n",
      "    4,3,3,3,3,3          67.3          0.958      112.9    0.88        496\n",
      "  4,3,3,3,3,3,3          77.6          0.978      113.2    1.00        244\n",
      "4,3,3,3,3,3,3,3         112.9          0.984      177.8    1.57        168\n",
      "Analysis saved!\n"
     ]
    }
   ],
   "source": [
    "if os.path.isfile(\"collection.anki21b\"):\n",
    "    os.remove(\"collection.anki21b\")\n",
    "    raise Exception(\n",
    "        \"Please export the file with `support older Anki versions` if you use the latest version of Anki.\")\n",
    "elif os.path.isfile(\"collection.anki21\"):\n",
    "    con = sqlite3.connect(\"collection.anki21\")\n",
    "elif os.path.isfile(\"collection.anki2\"):\n",
    "    con = sqlite3.connect(\"collection.anki2\")\n",
    "else:\n",
    "    raise Exception(\"Collection not exist!\")\n",
    "cur = con.cursor()\n",
    "res = cur.execute(\"SELECT * FROM revlog\")\n",
    "revlog = res.fetchall()\n",
    "\n",
    "df = pd.DataFrame(revlog)\n",
    "df.columns = ['id', 'cid', 'usn', 'r', 'ivl',\n",
    "              'last_lvl', 'factor', 'time', 'type']\n",
    "df = df[(df['cid'] <= time.time() * 1000) &\n",
    "        (df['id'] <= time.time() * 1000) &\n",
    "        (df['r'] > 0)].copy()\n",
    "df['create_date'] = pd.to_datetime(df['cid'] // 1000, unit='s')\n",
    "df['create_date'] = df['create_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df['review_date'] = pd.to_datetime(df['id'] // 1000, unit='s')\n",
    "df['review_date'] = df['review_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df.drop(df[df['review_date'].dt.year < 2006].index, inplace=True)\n",
    "df.sort_values(by=['cid', 'id'], inplace=True, ignore_index=True)\n",
    "type_sequence = np.array(df['type'])\n",
    "time_sequence = np.array(df['time'])\n",
    "df.to_csv(\"revlog.csv\", index=False)\n",
    "print(\"revlog.csv saved.\")\n",
    "df = df[df['type'] != 3].copy()\n",
    "df['real_days'] = df['review_date'] - timedelta(hours=next_day_starts_at)\n",
    "df['real_days'] = pd.DatetimeIndex(df['real_days'].dt.floor('D', ambiguous='infer', nonexistent='shift_forward')).to_julian_date()\n",
    "df.drop_duplicates(['cid', 'real_days'], keep='first', inplace=True)\n",
    "df['delta_t'] = df.real_days.diff()\n",
    "df.dropna(inplace=True)\n",
    "df['delta_t'] = df['delta_t'].astype(dtype=int)\n",
    "\n",
    "df['i'] = df.groupby('cid').cumcount() + 1\n",
    "df.loc[df['i'] == 1, 'delta_t'] = 0\n",
    "\n",
    "\n",
    "df = df.groupby('cid').filter(lambda group: group['type'].iloc[0] == 0)\n",
    "df['prev_type'] = df.groupby('cid')['type'].shift(1).fillna(0).astype(int)\n",
    "df['helper'] = (df['type'] == 0) & ((df['prev_type'] == 1) | (df['prev_type'] == 2)) & (df['i'] > 1)\n",
    "df['helper'] = df['helper'].astype(int)\n",
    "df['helper'] = df.groupby('cid')['helper'].cumsum()\n",
    "df = df[df['helper'] == 0]\n",
    "del df['prev_type']\n",
    "del df['helper']\n",
    "from itertools import accumulate\n",
    "\n",
    "def cum_concat(x):\n",
    "    return list(accumulate(x))\n",
    "\n",
    "t_history = df.groupby('cid', group_keys=False)['delta_t'].apply(lambda x: cum_concat([[i] for i in x]))\n",
    "df['t_history']=[','.join(map(str, item[:-1])) for sublist in t_history for item in sublist]\n",
    "r_history = df.groupby('cid', group_keys=False)['r'].apply(lambda x: cum_concat([[i] for i in x]))\n",
    "df['r_history']=[','.join(map(str, item[:-1])) for sublist in r_history for item in sublist]\n",
    "df = df[df['id'] >= time.mktime(datetime.strptime(revlog_start_date, \"%Y-%m-%d\").timetuple()) * 1000]\n",
    "df.to_csv('revlog_history.tsv', sep=\"\\t\", index=False)\n",
    "print(\"Trainset saved.\")\n",
    "\n",
    "df['y'] = df['r'].map(lambda x: {1: 0, 2: 1, 3: 1, 4: 1}[x])\n",
    "df['retention'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['y'].transform('mean')\n",
    "df['total_cnt'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['id'].transform('count')\n",
    "print(\"Retention calculated.\")\n",
    "df = df.drop(columns=['id', 'cid', 'usn', 'ivl', 'last_lvl', 'factor', 'time', 'type', 'create_date', 'review_date', 'real_days', 'r', 't_history', 'y'])\n",
    "df.drop_duplicates(inplace=True)\n",
    "df['retention'] = df['retention'].map(lambda x: max(min(0.99, x), 0.01))\n",
    "\n",
    "def cal_stability(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group_cnt = sum(group['total_cnt'])\n",
    "    if group_cnt < 10:\n",
    "        return pd.DataFrame()\n",
    "    group['group_cnt'] = group_cnt\n",
    "    if group['i'].values[0] > 1:\n",
    "        r_ivl_cnt = sum(group['delta_t'] * group['retention'].map(np.log) * pow(group['total_cnt'], 2))\n",
    "        ivl_ivl_cnt = sum(group['delta_t'].map(lambda x: x ** 2) * pow(group['total_cnt'], 2))\n",
    "        group['stability'] = round(np.log(0.9) / (r_ivl_cnt / ivl_ivl_cnt), 1)\n",
    "    else:\n",
    "        group['stability'] = 0.0\n",
    "    group['avg_retention'] = round(sum(group['retention'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 3)\n",
    "    group['avg_interval'] = round(sum(group['delta_t'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 1)\n",
    "    del group['total_cnt']\n",
    "    del group['retention']\n",
    "    del group['delta_t']\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history'], group_keys=False).progress_apply(cal_stability)\n",
    "print(\"Stability calculated.\")\n",
    "df.reset_index(drop = True, inplace = True)\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.sort_values(by=['r_history'], inplace=True, ignore_index=True)\n",
    "\n",
    "if df.shape[0] > 0:\n",
    "    for idx in notebook.tqdm(df.index):\n",
    "        item = df.loc[idx]\n",
    "        index = df[(df['i'] == item['i'] + 1) & (df['r_history'].str.startswith(item['r_history']))].index\n",
    "        df.loc[index, 'last_stability'] = item['stability']\n",
    "    df['factor'] = round(df['stability'] / df['last_stability'], 2)\n",
    "    df = df[(df['i'] >= 2) & (df['group_cnt'] >= 100)]\n",
    "    df['last_recall'] = df['r_history'].map(lambda x: x[-1])\n",
    "    df = df[df.groupby(['i', 'r_history'], group_keys=False)['group_cnt'].transform(max) == df['group_cnt']]\n",
    "    df.to_csv('./stability_for_analysis.tsv', sep='\\t', index=None)\n",
    "    print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "    print(df[df['r_history'].str.contains(r'^[1-4][^124]*$', regex=True)][['r_history', 'avg_interval', 'avg_retention', 'stability', 'factor', 'group_cnt']].to_string(index=False))\n",
    "    print(\"Analysis saved!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Optimize parameter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Define the model\n",
    "\n",
    "FSRS is a time-series model for predicting memory states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_w = [1, 1, 5, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2, 0.2, 0.2, 1]\n",
    "'''\n",
    "w[0]: initial_stability_for_again_answer\n",
    "w[1]: initial_stability_step_per_rating\n",
    "w[2]: initial_difficulty_for_good_answer\n",
    "w[3]: initial_difficulty_step_per_rating\n",
    "w[4]: next_difficulty_step_per_rating\n",
    "w[5]: next_difficulty_reversion_to_mean_speed (used to avoid ease hell)\n",
    "w[6]: next_stability_factor_after_success\n",
    "w[7]: next_stability_stabilization_decay_after_success\n",
    "w[8]: next_stability_retrievability_gain_after_success\n",
    "w[9]: next_stability_factor_after_failure\n",
    "w[10]: next_stability_difficulty_decay_after_success\n",
    "w[11]: next_stability_stability_gain_after_failure\n",
    "w[12]: next_stability_retrievability_gain_after_failure\n",
    "For more details about the parameters, please see: \n",
    "https://github.com/open-spaced-repetition/fsrs4anki/wiki/Free-Spaced-Repetition-Scheduler\n",
    "'''\n",
    "\n",
    "\n",
    "class FSRS(nn.Module):\n",
    "    def __init__(self, w):\n",
    "        super(FSRS, self).__init__()\n",
    "        self.w = nn.Parameter(torch.tensor(w))\n",
    "\n",
    "    def stability_after_success(self, state, new_d, r):\n",
    "        new_s = state[:,0] * (1 + torch.exp(self.w[6]) *\n",
    "                        (11 - new_d) *\n",
    "                        torch.pow(state[:,0], -self.w[7]) *\n",
    "                        (torch.exp((1 - r) * self.w[8]) - 1))\n",
    "        return new_s\n",
    "    \n",
    "    def stability_after_failure(self, state, new_d, r):\n",
    "        new_s = self.w[9] * torch.pow(new_d, -self.w[10]) * (torch.pow(\n",
    "            state[:,0] + 1, self.w[11]) - 1) * torch.exp((1 - r) * self.w[12])\n",
    "        return new_s\n",
    "\n",
    "    def step(self, X, state):\n",
    "        '''\n",
    "        :param X: shape[batch_size, 2], X[:,0] is elapsed time, X[:,1] is rating\n",
    "        :param state: shape[batch_size, 2], state[:,0] is stability, state[:,1] is difficulty\n",
    "        :return state:\n",
    "        '''\n",
    "        if torch.equal(state, torch.zeros_like(state)):\n",
    "            # first learn, init memory states\n",
    "            new_s = self.w[0] + self.w[1] * (X[:,1] - 1)\n",
    "            new_d = self.w[2] - self.w[3] * (X[:,1] - 3)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "        else:\n",
    "            r = torch.exp(np.log(0.9) * X[:,0] / state[:,0])\n",
    "            new_d = state[:,1] - self.w[4] * (X[:,1] - 3)\n",
    "            new_d = self.mean_reversion(self.w[2], new_d)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "            condition = X[:,1] > 1\n",
    "            new_s = torch.where(condition, self.stability_after_success(state, new_d, r), self.stability_after_failure(state, new_d, r))\n",
    "        new_s = new_s.clamp(0.1, 36500)\n",
    "        return torch.stack([new_s, new_d], dim=1)\n",
    "\n",
    "    def forward(self, inputs, state=None):\n",
    "        '''\n",
    "        :param inputs: shape[seq_len, batch_size, 2]\n",
    "        '''\n",
    "        if state is None:\n",
    "            state = torch.zeros((inputs.shape[1], 2))\n",
    "        else:\n",
    "            state, = state\n",
    "        outputs = []\n",
    "        for X in inputs:\n",
    "            state = self.step(X, state)\n",
    "            outputs.append(state)\n",
    "        return torch.stack(outputs), state\n",
    "\n",
    "    def mean_reversion(self, init, current):\n",
    "        return self.w[5] * init + (1-self.w[5]) * current\n",
    "\n",
    "\n",
    "class WeightClipper(object):\n",
    "    def __init__(self, frequency=1):\n",
    "        self.frequency = frequency\n",
    "\n",
    "    def __call__(self, module):\n",
    "        if hasattr(module, 'w'):\n",
    "            w = module.w.data\n",
    "            w[0] = w[0].clamp(0.1, 10)\n",
    "            w[1] = w[1].clamp(0.1, 5)\n",
    "            w[2] = w[2].clamp(1, 10)\n",
    "            w[3] = w[3].clamp(0.1, 5)\n",
    "            w[4] = w[4].clamp(0.1, 5)\n",
    "            w[5] = w[5].clamp(0.05, 0.5)\n",
    "            w[6] = w[6].clamp(0, 2)\n",
    "            w[7] = w[7].clamp(0.15, 0.8)\n",
    "            w[8] = w[8].clamp(0.01, 1.5)\n",
    "            w[9] = w[9].clamp(0.5, 5)\n",
    "            w[10] = w[10].clamp(0.01, 2)\n",
    "            w[11] = w[11].clamp(0.01, 0.9)\n",
    "            w[12] = w[12].clamp(0.01, 2)\n",
    "            module.w.data = w\n",
    "\n",
    "def lineToTensor(line):\n",
    "    ivl = line[0].split(',')\n",
    "    response = line[1].split(',')\n",
    "    tensor = torch.zeros(len(response), 2)\n",
    "    for li, response in enumerate(response):\n",
    "        tensor[li][0] = int(ivl[li])\n",
    "        tensor[li][1] = int(response)\n",
    "    return tensor\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader, Sampler\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n",
    "from typing import List\n",
    "\n",
    "class RevlogDataset(Dataset):\n",
    "    def __init__(self, dataframe):\n",
    "        padded = pad_sequence(dataframe['tensor'].to_list(), batch_first=True, padding_value=0)\n",
    "        self.x_train = padded.int()\n",
    "        self.t_train = torch.tensor(dataframe['delta_t'].values, dtype=torch.int)\n",
    "        self.y_train = torch.tensor(dataframe['y'].values, dtype=torch.float)\n",
    "        self.seq_len = torch.tensor(dataframe['tensor'].map(len).values, dtype=torch.long)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.x_train[idx], self.t_train[idx], self.y_train[idx], self.seq_len[idx]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y_train)\n",
    "    \n",
    "class RevlogSampler(Sampler[List[int]]):\n",
    "    def __init__(self, data_source, batch_size):\n",
    "        self.data_source = data_source\n",
    "        self.batch_size = batch_size\n",
    "        lengths = np.array(data_source.seq_len)\n",
    "        indices = np.argsort(lengths)\n",
    "        full_batches, remainder = divmod(indices.size, self.batch_size)\n",
    "        if full_batches > 0:\n",
    "            self.batch_indices = np.split(indices[:-remainder], full_batches)\n",
    "        else:\n",
    "            self.batch_indices = []\n",
    "        if remainder:\n",
    "            self.batch_indices.append(indices[-remainder:])\n",
    "        self.batch_nums = len(self.batch_indices)\n",
    "        # seed = int(torch.empty((), dtype=torch.int64).random_().item())\n",
    "        seed = 2023\n",
    "        self.generator = torch.Generator()\n",
    "        self.generator.manual_seed(seed)\n",
    "\n",
    "    def __iter__(self):\n",
    "        yield from [self.batch_indices[idx] for idx in torch.randperm(self.batch_nums, generator=self.generator).tolist()]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_source)\n",
    "\n",
    "\n",
    "def collate_fn(batch):\n",
    "    sequences, delta_ts, labels, seq_lens = zip(*batch)\n",
    "    sequences_packed = pack_padded_sequence(torch.stack(sequences, dim=1), lengths=torch.stack(seq_lens), batch_first=False, enforce_sorted=False)\n",
    "    sequences_padded, length = pad_packed_sequence(sequences_packed, batch_first=False)\n",
    "    sequences_padded = torch.as_tensor(sequences_padded)\n",
    "    seq_lens = torch.as_tensor(length)\n",
    "    delta_ts = torch.as_tensor(delta_ts)\n",
    "    labels = torch.as_tensor(labels)\n",
    "    return sequences_padded, delta_ts, labels, seq_lens\n",
    "\n",
    "class Optimizer(object):\n",
    "    def __init__(self, train_set, test_set, n_epoch=1, lr=1e-2, batch_size=256) -> None:\n",
    "        self.model = FSRS(init_w)\n",
    "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)\n",
    "        self.clipper = WeightClipper()\n",
    "        self.batch_size = batch_size\n",
    "        self.build_dataset(train_set, test_set)\n",
    "        self.n_epoch = n_epoch\n",
    "        self.batch_nums = self.next_train_data_loader.batch_sampler.batch_nums\n",
    "        self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=self.batch_nums * n_epoch)\n",
    "        self.avg_train_losses = []\n",
    "        self.avg_eval_losses = []\n",
    "        self.loss_fn = nn.BCELoss(reduction='sum')\n",
    "\n",
    "    def build_dataset(self, train_set, test_set):\n",
    "        pre_train_set = train_set[train_set['i'] == 2]\n",
    "        self.pre_train_set = RevlogDataset(pre_train_set)\n",
    "        sampler = RevlogSampler(self.pre_train_set, batch_size=self.batch_size)\n",
    "        self.pre_train_data_loader = DataLoader(self.pre_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        next_train_set = train_set[train_set['i'] > 2]\n",
    "        self.next_train_set = RevlogDataset(next_train_set)\n",
    "        sampler = RevlogSampler(self.next_train_set, batch_size=self.batch_size)\n",
    "        self.next_train_data_loader = DataLoader(self.next_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.train_set = RevlogDataset(train_set)\n",
    "        sampler = RevlogSampler(self.train_set, batch_size=self.batch_size)\n",
    "        self.train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.test_set = RevlogDataset(test_set)\n",
    "        sampler = RevlogSampler(self.test_set, batch_size=self.batch_size)\n",
    "        self.test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "        print(\"dataset built\")\n",
    "\n",
    "    def train(self):\n",
    "        # pretrain\n",
    "        best_loss = np.inf\n",
    "        weighted_loss, w = self.eval()\n",
    "        if weighted_loss < best_loss:\n",
    "            best_loss = weighted_loss\n",
    "            best_w = w\n",
    "\n",
    "        pbar = notebook.tqdm(desc=\"pre-train\", colour=\"red\", total=len(self.pre_train_data_loader) * self.n_epoch)\n",
    "        for k in range(self.n_epoch):\n",
    "            for i, batch in enumerate(self.pre_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                self.optimizer.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(n=real_batch_size)\n",
    "        pbar.close()\n",
    "\n",
    "        for name, param in self.model.named_parameters():\n",
    "            print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "\n",
    "        epoch_len = len(self.next_train_data_loader)\n",
    "        pbar = notebook.tqdm(desc=\"train\", colour=\"red\", total=epoch_len*self.n_epoch)\n",
    "        print_len = max(self.batch_nums*self.n_epoch // 10, 1)\n",
    "        for k in range(self.n_epoch):\n",
    "            weighted_loss, w = self.eval()\n",
    "            if weighted_loss < best_loss:\n",
    "                best_loss = weighted_loss\n",
    "                best_w = w\n",
    "            for i, batch in enumerate(self.next_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                for param in self.model.parameters():\n",
    "                    param.grad[:2] = torch.zeros(2)\n",
    "                self.optimizer.step()\n",
    "                self.scheduler.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(real_batch_size)\n",
    "\n",
    "                if (k * self.batch_nums + i + 1) % print_len == 0:\n",
    "                    print(f\"iteration: {k * epoch_len + (i + 1) * self.batch_size}\")\n",
    "                    for name, param in self.model.named_parameters():\n",
    "                        print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "        pbar.close()\n",
    "\n",
    "        weighted_loss, w = self.eval()\n",
    "        if weighted_loss < best_loss:\n",
    "            best_loss = weighted_loss\n",
    "            best_w = w\n",
    "\n",
    "        return best_w\n",
    "\n",
    "    def eval(self):\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            sequences, delta_ts, labels, seq_lens = self.train_set.x_train, self.train_set.t_train, self.train_set.y_train, self.train_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            tran_loss = self.loss_fn(retentions, labels)/len(self.train_set)\n",
    "            self.avg_train_losses.append(tran_loss)\n",
    "            print(f\"Loss in trainset: {tran_loss:.4f}\")\n",
    "            \n",
    "            sequences, delta_ts, labels, seq_lens = self.test_set.x_train, self.test_set.t_train, self.test_set.y_train, self.test_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            test_loss = self.loss_fn(retentions, labels)/len(self.test_set)\n",
    "            self.avg_eval_losses.append(test_loss)\n",
    "            print(f\"Loss in testset: {test_loss:.4f}\")\n",
    "\n",
    "            w = list(map(lambda x: round(float(x), 4), dict(self.model.named_parameters())['w'].data))\n",
    "\n",
    "            weighted_loss = (tran_loss * len(self.train_set) + test_loss * len(self.test_set)) / (len(self.train_set) + len(self.test_set))\n",
    "\n",
    "            return weighted_loss, w\n",
    "\n",
    "    def plot(self):\n",
    "        plt.plot(self.avg_train_losses, label='train')\n",
    "        plt.plot(self.avg_eval_losses, label='test')\n",
    "        plt.xlabel('epoch')\n",
    "        plt.ylabel('loss')\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Train the model\n",
    "\n",
    "The [./revlog_history.tsv](./revlog_history.tsv) generated before will be used for training the FSRS model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc4c02d9207845d5a9add00d2d3ce78b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/223795 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorized!\n",
      "TRAIN: 178072 TEST: 45723\n",
      "dataset built\n",
      "Loss in trainset: 0.3506\n",
      "Loss in testset: 0.3071\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2391f42718ce4f298a3a477c5b290cef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/86855 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.04, 2.2707, 5.0, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3747db1747d6425392295a0cd0d9f909",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/803505 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3483\n",
      "Loss in testset: 0.2982\n",
      "iteration: 80384\n",
      "w: [1.142, 2.3055, 3.8916, 1.9998, 1.6989, 0.05, 1.1569, 0.1911, 0.9974, 2.3923, 0.3411, 0.4261, 1.8341]\n",
      "iteration: 160768\n",
      "w: [1.142, 2.3055, 3.691, 1.7725, 1.7146, 0.05, 1.18, 0.1802, 1.0785, 2.4751, 0.2619, 0.3532, 1.8251]\n",
      "Loss in trainset: 0.3273\n",
      "Loss in testset: 0.2790\n",
      "iteration: 241085\n",
      "w: [1.142, 2.3055, 3.5485, 1.8033, 1.7556, 0.05, 1.1107, 0.191, 1.0577, 2.4405, 0.2945, 0.2817, 1.4476]\n",
      "iteration: 321469\n",
      "w: [1.142, 2.3055, 3.4979, 1.7839, 1.6101, 0.05, 1.0641, 0.1626, 0.9851, 2.5167, 0.2273, 0.3566, 1.5482]\n",
      "Loss in trainset: 0.3267\n",
      "Loss in testset: 0.2787\n",
      "iteration: 401786\n",
      "w: [1.142, 2.3055, 3.568, 1.9268, 1.6344, 0.05, 1.13, 0.1746, 1.0444, 2.5653, 0.188, 0.3707, 1.3234]\n",
      "iteration: 482170\n",
      "w: [1.142, 2.3055, 3.5804, 1.967, 1.9012, 0.05, 1.0829, 0.1505, 0.9816, 2.5196, 0.2108, 0.3266, 1.277]\n",
      "Loss in trainset: 0.3273\n",
      "Loss in testset: 0.2795\n",
      "iteration: 562487\n",
      "w: [1.142, 2.3055, 3.4471, 1.8832, 1.744, 0.05, 1.1246, 0.1503, 0.9996, 2.5675, 0.173, 0.3583, 1.3585]\n",
      "iteration: 642871\n",
      "w: [1.142, 2.3055, 3.4305, 1.9057, 1.6611, 0.05, 1.0779, 0.1526, 0.9326, 2.5799, 0.1546, 0.3639, 1.278]\n",
      "Loss in trainset: 0.3266\n",
      "Loss in testset: 0.2787\n",
      "iteration: 723188\n",
      "w: [1.142, 2.3055, 3.4061, 1.8682, 1.6238, 0.05, 1.0836, 0.1577, 0.9317, 2.566, 0.169, 0.3462, 1.2343]\n",
      "iteration: 803572\n",
      "w: [1.142, 2.3055, 3.4097, 1.8687, 1.6309, 0.05, 1.079, 0.158, 0.9256, 2.5757, 0.1586, 0.3542, 1.244]\n",
      "Loss in trainset: 0.3265\n",
      "Loss in testset: 0.2787\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 181664 TEST: 42131\n",
      "dataset built\n",
      "Loss in trainset: 0.3304\n",
      "Loss in testset: 0.3906\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f5f2214a60034e7dafa27bc5b37446df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/104865 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.7799, 2.6764, 5.0, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "53542ed45b0846398e8f90808aa3d341",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/803455 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3253\n",
      "Loss in testset: 0.3933\n",
      "iteration: 80384\n",
      "w: [1.8457, 2.7549, 4.0172, 1.8863, 1.5495, 0.05, 1.0503, 0.1972, 0.8545, 2.1564, 0.2794, 0.3459, 1.8595]\n",
      "iteration: 160768\n",
      "w: [1.8457, 2.7549, 3.9264, 1.6533, 1.4579, 0.05, 1.0893, 0.1604, 1.0075, 2.2124, 0.3232, 0.4691, 1.7676]\n",
      "Loss in trainset: 0.3053\n",
      "Loss in testset: 0.3731\n",
      "iteration: 241075\n",
      "w: [1.8457, 2.7549, 4.0143, 1.8903, 1.6515, 0.05, 1.1019, 0.1513, 1.0453, 2.3276, 0.2278, 0.4216, 1.6198]\n",
      "iteration: 321459\n",
      "w: [1.8457, 2.7549, 3.7385, 1.6318, 1.3343, 0.05, 1.0546, 0.1559, 0.9804, 2.2851, 0.3273, 0.4725, 1.7699]\n",
      "Loss in trainset: 0.3055\n",
      "Loss in testset: 0.3732\n",
      "iteration: 401766\n",
      "w: [1.8457, 2.7549, 3.9014, 1.8882, 1.6014, 0.05, 1.0779, 0.1667, 0.9688, 2.3093, 0.2966, 0.395, 1.5474]\n",
      "iteration: 482150\n",
      "w: [1.8457, 2.7549, 3.8767, 1.7898, 1.6194, 0.05, 1.0955, 0.15, 0.9542, 2.3496, 0.2287, 0.3845, 1.5153]\n",
      "Loss in trainset: 0.3051\n",
      "Loss in testset: 0.3729\n",
      "iteration: 562457\n",
      "w: [1.8457, 2.7549, 3.8701, 1.7265, 1.6569, 0.05, 1.1044, 0.1632, 0.9293, 2.3505, 0.2283, 0.3473, 1.4208]\n",
      "iteration: 642841\n",
      "w: [1.8457, 2.7549, 3.8057, 1.7053, 1.4708, 0.05, 1.084, 0.1509, 0.8897, 2.3804, 0.1927, 0.3806, 1.4291]\n",
      "Loss in trainset: 0.3050\n",
      "Loss in testset: 0.3728\n",
      "iteration: 723148\n",
      "w: [1.8457, 2.7549, 3.8063, 1.7181, 1.4711, 0.05, 1.1044, 0.1536, 0.9004, 2.3819, 0.1888, 0.377, 1.4112]\n",
      "iteration: 803532\n",
      "w: [1.8457, 2.7549, 3.8074, 1.7169, 1.4683, 0.05, 1.0998, 0.1505, 0.8944, 2.384, 0.1863, 0.3789, 1.4113]\n",
      "Loss in trainset: 0.3050\n",
      "Loss in testset: 0.3728\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 184379 TEST: 39416\n",
      "dataset built\n",
      "Loss in trainset: 0.3441\n",
      "Loss in testset: 0.3308\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7bbce4418f1d430697cf48f7077e5fb8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/143650 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.0719, 3.0637, 5.0, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "35ad7616a5684fa8936522856d216b99",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/778245 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3392\n",
      "Loss in testset: 0.3277\n",
      "iteration: 77824\n",
      "w: [1.0144, 2.9953, 4.2893, 1.8242, 2.0438, 0.0572, 1.2347, 0.15, 1.0864, 2.3498, 0.2766, 0.4797, 1.6558]\n",
      "iteration: 155648\n",
      "w: [1.0144, 2.9953, 3.7895, 1.8173, 1.4795, 0.05, 0.9277, 0.2179, 0.9313, 2.4469, 0.211, 0.4403, 1.7585]\n",
      "Loss in trainset: 0.3201\n",
      "Loss in testset: 0.3079\n",
      "iteration: 233217\n",
      "w: [1.0144, 2.9953, 3.5305, 1.6969, 1.6111, 0.05, 0.944, 0.15, 0.9798, 2.4644, 0.2229, 0.3723, 1.581]\n",
      "iteration: 311041\n",
      "w: [1.0144, 2.9953, 3.4481, 1.7001, 1.5207, 0.05, 1.0516, 0.15, 1.0726, 2.4896, 0.1996, 0.3674, 1.4444]\n",
      "Loss in trainset: 0.3190\n",
      "Loss in testset: 0.3068\n",
      "iteration: 388610\n",
      "w: [1.0144, 2.9953, 3.5314, 1.6721, 1.4842, 0.05, 0.9825, 0.1748, 0.9661, 2.5766, 0.0745, 0.3781, 1.3467]\n",
      "iteration: 466434\n",
      "w: [1.0144, 2.9953, 3.58, 1.9097, 1.7899, 0.0504, 1.0428, 0.15, 1.0127, 2.5278, 0.1445, 0.3704, 1.3028]\n",
      "Loss in trainset: 0.3188\n",
      "Loss in testset: 0.3067\n",
      "iteration: 544003\n",
      "w: [1.0144, 2.9953, 3.5519, 1.8748, 1.6711, 0.05, 1.065, 0.1537, 1.0041, 2.5509, 0.1274, 0.3604, 1.3031]\n",
      "iteration: 621827\n",
      "w: [1.0144, 2.9953, 3.4403, 1.7393, 1.596, 0.05, 1.0306, 0.15, 0.9518, 2.5531, 0.1212, 0.343, 1.2483]\n",
      "Loss in trainset: 0.3184\n",
      "Loss in testset: 0.3062\n",
      "iteration: 699396\n",
      "w: [1.0144, 2.9953, 3.427, 1.7089, 1.5981, 0.0506, 1.0319, 0.15, 0.9436, 2.5519, 0.1199, 0.3289, 1.2289]\n",
      "iteration: 777220\n",
      "w: [1.0144, 2.9953, 3.4406, 1.7269, 1.5997, 0.05, 1.0224, 0.15, 0.9325, 2.5542, 0.1176, 0.3313, 1.2221]\n",
      "Loss in trainset: 0.3183\n",
      "Loss in testset: 0.3063\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 175532 TEST: 48263\n",
      "dataset built\n",
      "Loss in trainset: 0.3448\n",
      "Loss in testset: 0.3306\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e7fd7d649d724b80abec26e1dfd6ce09",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/99180 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.0376, 3.5276, 5.0, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad5c8fe1b3a0433c973c1bcab41923bf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/778480 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3398\n",
      "Loss in testset: 0.3265\n",
      "iteration: 77824\n",
      "w: [1.0208, 3.4792, 4.2833, 1.5442, 1.5522, 0.05, 1.1965, 0.15, 1.0918, 2.2837, 0.1667, 0.4862, 1.5238]\n",
      "iteration: 155648\n",
      "w: [1.0208, 3.4792, 3.6892, 1.7868, 1.4009, 0.0538, 1.0617, 0.15, 1.0254, 2.2601, 0.2953, 0.4322, 1.8857]\n",
      "Loss in trainset: 0.3197\n",
      "Loss in testset: 0.3063\n",
      "iteration: 233264\n",
      "w: [1.0208, 3.4792, 3.6044, 1.6492, 1.226, 0.05, 1.0028, 0.15, 1.0353, 2.3977, 0.2318, 0.3839, 1.878]\n",
      "iteration: 311088\n",
      "w: [1.0208, 3.4792, 3.6547, 1.7235, 1.6226, 0.05, 0.9979, 0.15, 0.9858, 2.387, 0.2303, 0.362, 1.5983]\n",
      "Loss in trainset: 0.3193\n",
      "Loss in testset: 0.3056\n",
      "iteration: 388704\n",
      "w: [1.0208, 3.4792, 3.5938, 1.5304, 1.5148, 0.05, 0.9881, 0.1951, 0.9721, 2.4369, 0.185, 0.3714, 1.3995]\n",
      "iteration: 466528\n",
      "w: [1.0208, 3.4792, 3.5616, 1.6805, 1.5512, 0.05, 0.9755, 0.15, 0.9216, 2.4899, 0.1204, 0.3849, 1.4409]\n",
      "Loss in trainset: 0.3194\n",
      "Loss in testset: 0.3060\n",
      "iteration: 544144\n",
      "w: [1.0208, 3.4792, 3.5018, 1.6832, 1.4689, 0.05, 0.9967, 0.1564, 0.92, 2.4737, 0.1357, 0.3568, 1.4295]\n",
      "iteration: 621968\n",
      "w: [1.0208, 3.4792, 3.421, 1.5868, 1.4109, 0.0503, 1.0449, 0.15, 0.9522, 2.4711, 0.1366, 0.349, 1.3064]\n",
      "Loss in trainset: 0.3192\n",
      "Loss in testset: 0.3061\n",
      "iteration: 699584\n",
      "w: [1.0208, 3.4792, 3.4687, 1.6173, 1.4784, 0.05, 1.0203, 0.1528, 0.9181, 2.4683, 0.1394, 0.3388, 1.2926]\n",
      "iteration: 777408\n",
      "w: [1.0208, 3.4792, 3.471, 1.6248, 1.4762, 0.05, 1.0215, 0.15, 0.9178, 2.476, 0.1313, 0.3464, 1.2938]\n",
      "Loss in trainset: 0.3189\n",
      "Loss in testset: 0.3056\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 175533 TEST: 48262\n",
      "dataset built\n",
      "Loss in trainset: 0.3389\n",
      "Loss in testset: 0.3520\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "195b999fb7854dada21fc3e87782b3d1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/144850 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [0.9101, 2.8883, 5.0, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e736c2f20964463a99c4480020c33d1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/732815 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3340\n",
      "Loss in testset: 0.3504\n",
      "iteration: 73216\n",
      "w: [0.6759, 2.818, 3.942, 2.0145, 1.3462, 0.05, 0.9111, 0.168, 1.0648, 2.4224, 0.268, 0.4321, 1.9156]\n",
      "iteration: 146432\n",
      "w: [0.6759, 2.818, 3.7176, 1.4556, 1.5554, 0.05, 0.9101, 0.1806, 1.1331, 2.5089, 0.212, 0.441, 1.8961]\n",
      "Loss in trainset: 0.3156\n",
      "Loss in testset: 0.3306\n",
      "iteration: 219523\n",
      "w: [0.6759, 2.818, 3.5525, 1.8383, 1.6052, 0.0969, 0.9924, 0.15, 1.2071, 2.5178, 0.2137, 0.3192, 1.9076]\n",
      "iteration: 292739\n",
      "w: [0.6759, 2.818, 3.4704, 2.011, 1.346, 0.05, 0.8374, 0.15, 1.0648, 2.5273, 0.2332, 0.4024, 1.7474]\n",
      "Loss in trainset: 0.3145\n",
      "Loss in testset: 0.3296\n",
      "iteration: 365830\n",
      "w: [0.6759, 2.818, 3.3846, 1.9045, 1.713, 0.05, 0.9471, 0.15, 1.1655, 2.5899, 0.1586, 0.3613, 1.9884]\n",
      "iteration: 439046\n",
      "w: [0.6759, 2.818, 3.3191, 1.8344, 1.5266, 0.05, 0.8884, 0.1517, 1.058, 2.5034, 0.2513, 0.3135, 1.831]\n",
      "Loss in trainset: 0.3154\n",
      "Loss in testset: 0.3296\n",
      "iteration: 512137\n",
      "w: [0.6759, 2.818, 3.3088, 1.7681, 1.5055, 0.05, 0.9018, 0.1634, 1.042, 2.5701, 0.1703, 0.3646, 1.8479]\n",
      "iteration: 585353\n",
      "w: [0.6759, 2.818, 3.2938, 1.7792, 1.5984, 0.05, 0.9426, 0.15, 1.0625, 2.5661, 0.18, 0.3408, 1.8063]\n",
      "Loss in trainset: 0.3141\n",
      "Loss in testset: 0.3286\n",
      "iteration: 658444\n",
      "w: [0.6759, 2.818, 3.3219, 1.8003, 1.6451, 0.05, 0.9258, 0.1509, 1.0351, 2.57, 0.1744, 0.3377, 1.7869]\n",
      "iteration: 731660\n",
      "w: [0.6759, 2.818, 3.3172, 1.7911, 1.6379, 0.05, 0.9248, 0.15, 1.0327, 2.5807, 0.1628, 0.3487, 1.7935]\n",
      "Loss in trainset: 0.3141\n",
      "Loss in testset: 0.3287\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training finished!\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"./revlog_history.tsv\", sep='\\t', index_col=None, dtype={'r_history': str ,'t_history': str} )\n",
    "dataset = dataset[(dataset['i'] > 1) & (dataset['delta_t'] > 0) & (dataset['t_history'].str.count(',0') == 0)]\n",
    "dataset['tensor'] = dataset.progress_apply(lambda x: lineToTensor(list(zip([x['t_history']], [x['r_history']]))[0]), axis=1)\n",
    "dataset['y'] = dataset['r'].map({1: 0, 2: 1, 3: 1, 4: 1})\n",
    "dataset['group'] = dataset['r_history'] + dataset['t_history']\n",
    "print(\"Tensorized!\")\n",
    "\n",
    "from sklearn.model_selection import StratifiedGroupKFold\n",
    "\n",
    "w = []\n",
    "\n",
    "sgkf = StratifiedGroupKFold(n_splits=5)\n",
    "for train_index, test_index in sgkf.split(dataset, dataset['i'], dataset['group']):\n",
    "    print(\"TRAIN:\", len(train_index), \"TEST:\",  len(test_index))\n",
    "    train_set = dataset.iloc[train_index].copy()\n",
    "    test_set = dataset.iloc[test_index].copy()\n",
    "    optimizer = Optimizer(train_set, test_set, n_epoch=5, lr=4e-2, batch_size=256)\n",
    "    w.append(optimizer.train())\n",
    "    optimizer.plot()\n",
    "\n",
    "print(\"\\nTraining finished!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Result\n",
    "\n",
    "Copy the optimal parameters for FSRS for you in the output of next code cell after running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.1398, 2.8706, 3.4892, 1.7457, 1.5626, 0.05, 1.0295, 0.1517, 0.9406, 2.5141, 0.1513, 0.3519, 1.3929]\n"
     ]
    }
   ],
   "source": [
    "w = np.array(w)\n",
    "avg_w = np.round(np.mean(w, axis=0), 4)\n",
    "print(avg_w.tolist())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Preview\n",
    "\n",
    "You can see the memory states and intervals generated by FSRS as if you press the good in each review at the due date scheduled by FSRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "first rating: 1\n",
      "rating history: 1,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,1d,2d,4d,8d,16d,1.0m,1.8m,3.2m,5.5m,9.2m\n",
      "difficulty history: 0,7.0,6.8,6.6,6.5,6.3,6.2,6.1,5.9,5.8,5.7\n",
      "\n",
      "first rating: 2\n",
      "rating history: 2,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,4d,9d,20d,1.4m,2.7m,5.0m,9.0m,1.3y,2.2y,3.6y\n",
      "difficulty history: 0,5.2,5.1,5.1,5.0,4.9,4.8,4.8,4.7,4.6,4.6\n",
      "\n",
      "first rating: 3\n",
      "rating history: 3,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,7d,18d,1.4m,3.0m,6.2m,12.0m,1.8y,3.2y,5.5y,9.2y\n",
      "difficulty history: 0,3.5,3.5,3.5,3.5,3.5,3.5,3.5,3.5,3.5,3.5\n",
      "\n",
      "first rating: 4\n",
      "rating history: 4,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,10d,28d,2.3m,5.4m,11.5m,1.9y,3.6y,6.5y,11.3y,19.0y\n",
      "difficulty history: 0,1.7,1.8,1.9,2.0,2.1,2.1,2.2,2.3,2.3,2.4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "\n",
    "\n",
    "class Collection:\n",
    "    def __init__(self, w):\n",
    "        self.model = FSRS(w)\n",
    "        self.model.eval()\n",
    "\n",
    "    def predict(self, t_history, r_history):\n",
    "        with torch.no_grad():\n",
    "            line_tensor = lineToTensor(list(zip([t_history], [r_history]))[0]).unsqueeze(1)\n",
    "            output_t = self.model(line_tensor)\n",
    "            return output_t[-1][0]\n",
    "        \n",
    "    def batch_predict(self, dataset):\n",
    "        with torch.no_grad():\n",
    "            fast_dataset = RevlogDataset(dataset)\n",
    "            outputs, _ = self.model(fast_dataset.x_train.transpose(0, 1))\n",
    "            stabilities, difficulties = outputs[fast_dataset.seq_len-1, torch.arange(len(fast_dataset))].transpose(0, 1)\n",
    "            return stabilities.tolist(), difficulties.tolist()\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "for first_rating in (1,2,3,4):\n",
    "    print(f'first rating: {first_rating}')\n",
    "    t_history = \"0\"\n",
    "    d_history = \"0\"\n",
    "    r_history = f\"{first_rating}\"  # the first rating of the new card\n",
    "    # print(\"stability, difficulty, lapses\")\n",
    "    for i in range(10):\n",
    "        states = my_collection.predict(t_history, r_history)\n",
    "        # print('{0:9.2f} {1:11.2f} {2:7.0f}'.format(\n",
    "            # *list(map(lambda x: round(float(x), 4), states))))\n",
    "        next_t = max(round(float(np.log(requestRetention)/np.log(0.9) * states[0])), 1)\n",
    "        difficulty = round(float(states[1]), 1)\n",
    "        t_history += f',{int(next_t)}'\n",
    "        d_history += f',{difficulty}'\n",
    "        r_history += f\",3\"\n",
    "    print(f\"rating history: {r_history}\")\n",
    "    print(\"interval history: \" + \",\".join([f\"{ivl}d\" if ivl < 30 else f\"{ivl / 30:.1f}m\" if ivl < 365 else f\"{ivl / 365:.1f}y\" for ivl in map(int, t_history.split(','))]))\n",
    "    print(f\"difficulty history: {d_history}\")\n",
    "    print('')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the `test_rating_sequence` to see the scheduling intervals in different ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([6.8810, 3.4892])\n",
      "tensor([17.7143,  3.4892])\n",
      "tensor([41.8488,  3.4892])\n",
      "tensor([91.2813,  3.4892])\n",
      "tensor([186.4386,   3.4892])\n",
      "tensor([11.5615,  6.4581])\n",
      "tensor([2.9782, 9.2786])\n",
      "tensor([4.3897, 8.9892])\n",
      "tensor([6.4080, 8.7142])\n",
      "tensor([9.5928, 8.4529])\n",
      "tensor([15.0685,  8.2047])\n",
      "tensor([23.3876,  7.9690])\n",
      "rating history: 3,3,3,3,3,1,1,3,3,3,3,3\n",
      "interval history: 0,7,18,42,91,186,12,3,4,6,10,15,23\n",
      "difficulty history: 0,3.5,3.5,3.5,3.5,3.5,6.5,9.3,9.0,8.7,8.5,8.2,8.0\n"
     ]
    }
   ],
   "source": [
    "test_rating_sequence = \"3,3,3,3,3,1,1,3,3,3,3,3\"\n",
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "easyBonus = 1.3\n",
    "hardInterval = 1.2\n",
    "\n",
    "t_history = \"0\"\n",
    "d_history = \"0\"\n",
    "for i in range(len(test_rating_sequence.split(','))):\n",
    "    rating = test_rating_sequence[2*i]\n",
    "    last_t = int(t_history.split(',')[-1])\n",
    "    r_history = test_rating_sequence[:2*i+1]\n",
    "    states = my_collection.predict(t_history, r_history)\n",
    "    print(states)\n",
    "    next_t = max(1,round(float(np.log(requestRetention)/np.log(0.9) * states[0])))\n",
    "    if rating == '4':\n",
    "        next_t = round(next_t * easyBonus)\n",
    "    elif rating == '2':\n",
    "        next_t = round(last_t * hardInterval)\n",
    "    t_history += f',{int(next_t)}'\n",
    "    difficulty = round(float(states[1]), 1)\n",
    "    d_history += f',{difficulty}'\n",
    "print(f\"rating history: {test_rating_sequence}\")\n",
    "print(f\"interval history: {t_history}\")\n",
    "print(f\"difficulty history: {d_history}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Predict memory states and distribution of difficulty\n",
    "\n",
    "Predict memory states for each review group and save them in [./prediction.tsv](./prediction.tsv).\n",
    "\n",
    "Meanwhile, it will count the distribution of difficulty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction.tsv saved.\n",
      "difficulty\n",
      "1     0.048647\n",
      "2     0.146808\n",
      "3     0.158087\n",
      "4     0.015532\n",
      "5     0.064555\n",
      "6     0.068161\n",
      "7     0.094524\n",
      "8     0.073853\n",
      "9     0.144579\n",
      "10    0.185254\n",
      "Name: count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "stabilities = map(lambda x: round(x, 2), stabilities)\n",
    "difficulties = map(lambda x: round(x, 2), difficulties)\n",
    "dataset['stability'] = list(stabilities)\n",
    "dataset['difficulty'] = list(difficulties)\n",
    "prediction = dataset.groupby(by=['t_history', 'r_history']).agg({\"stability\": \"mean\", \"difficulty\": \"mean\", \"id\": \"count\"})\n",
    "prediction.reset_index(inplace=True)\n",
    "prediction.sort_values(by=['r_history'], inplace=True)\n",
    "prediction.rename(columns={\"id\": \"count\"}, inplace=True)\n",
    "prediction.to_csv(\"./prediction.tsv\", sep='\\t', index=None)\n",
    "print(\"prediction.tsv saved.\")\n",
    "prediction['difficulty'] = prediction['difficulty'].map(lambda x: int(round(x)))\n",
    "difficulty_distribution = prediction.groupby(by=['difficulty'])['count'].sum() / prediction['count'].sum()\n",
    "print(difficulty_distribution)\n",
    "difficulty_distribution_padding = np.zeros(10)\n",
    "for i in range(10):\n",
    "    if i+1 in difficulty_distribution.index:\n",
    "        difficulty_distribution_padding[i] = difficulty_distribution.loc[i+1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Optimize retention to minimize the time of reviews\n",
    "\n",
    "Calculate the optimal retention to minimize the time for long-term memory consolidation. It is an experimental feature. You can use the simulator to get more accurate results:\n",
    "\n",
    "https://github.com/open-spaced-repetition/fsrs4anki/blob/main/fsrs4anki_simulator.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average time for failed cards: 25.0s\n",
      "average time for recalled cards: 8.0s\n",
      "terminal stability:  740.27\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "149e50a7706347fcaf42bf18ad2cc485",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "find optimal retention:   0%|          | 0/15 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected_time.csv saved.\n",
      "\n",
      "-----suggested retention (experimental): 0.84-----\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base = 1.01\n",
    "minimum_stability = 0.1\n",
    "index_offset = -(np.log(minimum_stability) / np.log(base)).round().astype(int)\n",
    "d_range = 10\n",
    "d_offset = 1\n",
    "r_time = 8\n",
    "f_time = 25\n",
    "max_time = 1e10\n",
    "\n",
    "type_block = dict()\n",
    "type_count = dict()\n",
    "type_time = dict()\n",
    "last_t = type_sequence[0]\n",
    "type_block[last_t] = 1\n",
    "type_count[last_t] = 1\n",
    "type_time[last_t] = time_sequence[0]\n",
    "for i,t in enumerate(type_sequence[1:]):\n",
    "    type_count[t] = type_count.setdefault(t, 0) + 1\n",
    "    type_time[t] = type_time.setdefault(t, 0) + time_sequence[i]\n",
    "    if t != last_t:\n",
    "        type_block[t] = type_block.setdefault(t, 0) + 1\n",
    "    last_t = t\n",
    "\n",
    "r_time = round(type_time[1]/type_count[1]/1000, 1)\n",
    "\n",
    "if 2 in type_count and 2 in type_block:\n",
    "    f_time = round(type_time[2]/type_block[2]/1000 + r_time, 1)\n",
    "\n",
    "print(f\"average time for failed cards: {f_time}s\")\n",
    "print(f\"average time for recalled cards: {r_time}s\")\n",
    "\n",
    "def stability2index(stability):\n",
    "    return (np.log(stability) / np.log(base)).round().astype(int) + index_offset\n",
    "\n",
    "def init_stability(d):\n",
    "    return max(((d - avg_w[2]) / -avg_w[3] + 2) * avg_w[1] + avg_w[0], np.power(base, -index_offset))\n",
    "\n",
    "def cal_next_recall_stability(s, r, d, response):\n",
    "    if response == 1:\n",
    "        return s * (1 + np.exp(avg_w[6]) * (11 - d) * np.power(s, -avg_w[7]) * (np.exp((1 - r) * avg_w[8]) - 1))\n",
    "    else:\n",
    "        return avg_w[9] * np.power(d, -avg_w[10]) * (np.power(s + 1, avg_w[11]) - 1) * np.exp((1 - r) * avg_w[12])\n",
    "\n",
    "terminal_stability = minimum_stability\n",
    "for _ in range(128):\n",
    "    terminal_stability = cal_next_recall_stability(terminal_stability, 0.96, 10, 1)\n",
    "index_len = stability2index(terminal_stability)\n",
    "stability_list = np.array([np.power(base, i - index_offset) for i in range(index_len)])\n",
    "print(f\"terminal stability: {stability_list.max(): .2f}\")\n",
    "df = pd.DataFrame(columns=[\"retention\", \"difficulty\", \"time\"])\n",
    "\n",
    "for percentage in notebook.tqdm(range(96, 66, -2), desc=\"find optimal retention\"):\n",
    "    recall = percentage / 100\n",
    "    time_list = np.zeros((d_range, index_len))\n",
    "    time_list[:,:-1] = max_time\n",
    "    for d in range(d_range, 0, -1):\n",
    "        s0 = init_stability(d)\n",
    "        s0_index = stability2index(s0)\n",
    "        diff = max_time\n",
    "        iteration = 0\n",
    "        while diff > 1 and iteration < 2e5:\n",
    "            iteration += 1\n",
    "            total_time = time_list[d - 1].sum()\n",
    "            s_indices = np.arange(index_len - 2, -1, -1)\n",
    "            stabilities = stability_list[s_indices]\n",
    "            intervals = np.maximum(1, np.round(stabilities * np.log(recall) / np.log(0.9)))\n",
    "            p_recalls = np.power(0.9, intervals / stabilities)\n",
    "            recall_s = cal_next_recall_stability(stabilities, p_recalls, d, 1)\n",
    "            forget_d = np.minimum(d + d_offset, 10)\n",
    "            forget_s = cal_next_recall_stability(stabilities, p_recalls, forget_d, 0)\n",
    "            recall_s_indices = np.minimum(stability2index(recall_s), index_len - 1)\n",
    "            forget_s_indices = np.clip(stability2index(forget_s), 0, index_len - 1)\n",
    "            recall_times = time_list[d - 1][recall_s_indices] + r_time\n",
    "            forget_times = time_list[forget_d - 1][forget_s_indices] + f_time\n",
    "            exp_times = p_recalls * recall_times + (1.0 - p_recalls) * forget_times\n",
    "            mask = exp_times <= time_list[d - 1][s_indices]\n",
    "            time_list[d - 1][s_indices[mask]] = exp_times[mask]\n",
    "            diff = total_time - time_list[d - 1].sum()\n",
    "            s0_time = time_list[d - 1][s0_index]\n",
    "            if iteration % 1e5 == 0:\n",
    "                print(f\"iteration: {iteration}, diff: {diff}, s0_time: {s0_time}\")\n",
    "        df.loc[0 if pd.isnull(df.index.max()) else df.index.max() + 1] = [recall, d, s0_time]\n",
    "\n",
    "df.sort_values(by=[\"difficulty\", \"retention\"], inplace=True)\n",
    "df.to_csv(\"./expected_time.csv\", index=False)\n",
    "print(\"expected_time.csv saved.\")\n",
    "\n",
    "optimal_retention_list = np.zeros(10)\n",
    "for d in range(1, d_range+1):\n",
    "    retention = df[df[\"difficulty\"] == d][\"retention\"]\n",
    "    time = df[df[\"difficulty\"] == d][\"time\"]\n",
    "    optimal_retention = retention.iat[time.argmin()]\n",
    "    optimal_retention_list[d-1] = optimal_retention\n",
    "    plt.plot(retention, time, label=f\"d={d}, r={optimal_retention}\")\n",
    "print(f\"\\n-----suggested retention (experimental): {np.inner(difficulty_distribution_padding, optimal_retention_list):.2f}-----\")\n",
    "plt.ylabel(\"expected time (second)\")\n",
    "plt.xlabel(\"retention\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.semilogy()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Evaluate the model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Loss\n",
    "\n",
    "Evaluate the model with the log loss. It will compare the log loss between initial model and trained model.\n",
    "\n",
    "And it will predict the stability, difficulty and retrievability for each revlog in [./evaluation.tsv](./evaluation.tsv)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss before training: 0.3418\n",
      "Loss after training: 0.3164\n"
     ]
    }
   ],
   "source": [
    "my_collection = Collection(init_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss before training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss after training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "tmp = dataset.copy()\n",
    "tmp['stability'] = tmp['stability'].map(lambda x: round(x, 2))\n",
    "tmp['difficulty'] = tmp['difficulty'].map(lambda x: round(x, 2))\n",
    "tmp['p'] = tmp['p'].map(lambda x: round(x, 2))\n",
    "tmp['log_loss'] = tmp['log_loss'].map(lambda x: round(x, 2))\n",
    "tmp.rename(columns={\"r\": \"grade\", \"p\": \"retrievability\"}, inplace=True)\n",
    "tmp[['id', 'cid', 'review_date', 'r_history', 't_history', 'delta_t', 'grade', 'stability', 'difficulty', 'retrievability', 'log_loss']].to_csv(\"./evaluation.tsv\", sep='\\t', index=False)\n",
    "del tmp"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Calibration graph\n",
    "\n",
    "1. FSRS predicts the stability and retention for each review.\n",
    "2. Reviews are grouped into 40 bins according to their predicted retention.\n",
    "3. Count the true retention of each bin.\n",
    "4. Plot (predicted retention, true retention) in the line graph.\n",
    "5. Plot (predicted retention, size of bin) in the bar graph.\n",
    "6. Combine these graphs to create the calibration graph.\n",
    "\n",
    "Ideally, the blue line should be aligned with the orange one. If the blue line is higher than the orange line, the FSRS underestimates the retention. When the size of reviews within a bin is small, actual retention may deviate largely, which is normal.\n",
    "\n",
    "R-squared (aka the coefficient of determination), is the proportion of the variation in the dependent variable that is predictable from the independent variable(s). The higher the R-squared, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Coefficient_of_determination\n",
    "\n",
    "RMSE (root mean squared error) is the square root of the average of squared differences between prediction and actual observation. The lower the RMSE, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Root-mean-square_deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.9232\n",
      "RMSE: 0.0173\n",
      "[0.14319141 0.83932372]\n",
      "Last rating: 1\n",
      "R-squared: 0.2528\n",
      "RMSE: 0.0631\n",
      "[0.32721173 0.59919747]\n",
      "Last rating: 2\n",
      "R-squared: 0.3361\n",
      "RMSE: 0.0450\n",
      "[-0.12413612  1.08848104]\n",
      "Last rating: 3\n",
      "R-squared: 0.9301\n",
      "RMSE: 0.0184\n",
      "[0.03963609 0.9676909 ]\n",
      "Last rating: 4\n",
      "R-squared: 0.2861\n",
      "RMSE: 0.0336\n",
      "[0.3389664  0.65492574]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import statsmodels.api as sm\n",
    "\n",
    "\n",
    "# code from https://github.com/papousek/duolingo-halflife-regression/blob/master/evaluation.py\n",
    "def load_brier(predictions, real, bins=20):\n",
    "    counts = np.zeros(bins)\n",
    "    correct = np.zeros(bins)\n",
    "    prediction = np.zeros(bins)\n",
    "    for p, r in zip(predictions, real):\n",
    "        bin = min(int(p * bins), bins - 1)\n",
    "        counts[bin] += 1\n",
    "        correct[bin] += r\n",
    "        prediction[bin] += p\n",
    "    np.seterr(invalid='ignore')\n",
    "    prediction_means = prediction / counts\n",
    "    prediction_means[np.isnan(prediction_means)] = ((np.arange(bins) + 0.5) / bins)[np.isnan(prediction_means)]\n",
    "    correct_means = correct / counts\n",
    "    correct_means[np.isnan(correct_means)] = 0\n",
    "    size = len(predictions)\n",
    "    answer_mean = sum(correct) / size\n",
    "    return {\n",
    "        \"reliability\": sum(counts * (correct_means - prediction_means) ** 2) / size,\n",
    "        \"resolution\": sum(counts * (correct_means - answer_mean) ** 2) / size,\n",
    "        \"uncertainty\": answer_mean * (1 - answer_mean),\n",
    "        \"detail\": {\n",
    "            \"bin_count\": bins,\n",
    "            \"bin_counts\": list(counts),\n",
    "            \"bin_prediction_means\": list(prediction_means),\n",
    "            \"bin_correct_means\": list(correct_means),\n",
    "        }\n",
    "    }\n",
    "\n",
    "\n",
    "def plot_brier(predictions, real, bins=20):\n",
    "    brier = load_brier(predictions, real, bins=bins)\n",
    "    bin_prediction_means = brier['detail']['bin_prediction_means']\n",
    "    bin_correct_means = brier['detail']['bin_correct_means']\n",
    "    bin_counts = brier['detail']['bin_counts']\n",
    "    r2 = r2_score(bin_correct_means, bin_prediction_means, sample_weight=bin_counts)\n",
    "    rmse = mean_squared_error(bin_correct_means, bin_prediction_means, sample_weight=bin_counts, squared=False)\n",
    "    print(f\"R-squared: {r2:.4f}\")\n",
    "    print(f\"RMSE: {rmse:.4f}\")\n",
    "    plt.figure()\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.set_ylim([0, 1])\n",
    "    plt.grid(True)\n",
    "    fit_wls = sm.WLS(bin_correct_means, sm.add_constant(bin_prediction_means), weights=bin_counts).fit()\n",
    "    print(fit_wls.params)\n",
    "    y_regression = [fit_wls.params[0] + fit_wls.params[1]*x for x in bin_prediction_means]\n",
    "    plt.plot(bin_prediction_means, y_regression, label='Weighted Least Squares Regression', color=\"green\")\n",
    "    plt.plot(bin_prediction_means, bin_correct_means, label='Actual Calibration', color=\"#1f77b4\")\n",
    "    plt.plot((0, 1), (0, 1), label='Perfect Calibration', color=\"#ff7f0e\")\n",
    "    bin_count = brier['detail']['bin_count']\n",
    "    counts = np.array(bin_counts)\n",
    "    bins = (np.arange(bin_count) + 0.5) / bin_count\n",
    "    plt.legend(loc='upper center')\n",
    "    plt.xlabel('Predicted R')\n",
    "    plt.ylabel('Actual R')\n",
    "    plt.twinx()\n",
    "    plt.ylabel('Number of reviews')\n",
    "    plt.bar(bins, counts, width=(0.8 / bin_count), ec='k', lw=.2, alpha=0.5, label='Number of reviews')\n",
    "    plt.legend(loc='lower center')\n",
    "\n",
    "\n",
    "plot_brier(dataset['p'], dataset['y'], bins=40)\n",
    "for last_rating in (\"1\",\"2\",\"3\",\"4\"):\n",
    "    print(f\"Last rating: {last_rating}\")\n",
    "    plot_brier(dataset[dataset['r_history'].str.endswith(last_rating)]['p'], dataset[dataset['r_history'].str.endswith(last_rating)]['y'], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "\n",
    "def to_percent(temp, position):\n",
    "    return '%1.0f' % (100 * temp) + '%'\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "stability_calibration = pd.DataFrame(columns=['stability', 'predicted_retention', 'actual_retention'])\n",
    "stability_calibration = dataset[['stability', 'p', 'y']].copy()\n",
    "stability_calibration['bin'] = stability_calibration['stability'].map(lambda x: math.pow(1.2, math.floor(math.log(x, 1.2))))\n",
    "stability_group = stability_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=stability_group.index, height=stability_group['y'], width=stability_group.index / 5.5,\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Stability (days)\")\n",
    "ax1.semilogx()\n",
    "lns.append(lns1)\n",
    "\n",
    "stability_group = stability_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(stability_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(stability_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "difficulty_calibration = pd.DataFrame(columns=['difficulty', 'predicted_retention', 'actual_retention'])\n",
    "difficulty_calibration = dataset[['difficulty', 'p', 'y']].copy()\n",
    "difficulty_calibration['bin'] = difficulty_calibration['difficulty'].map(round)\n",
    "difficulty_group = difficulty_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=difficulty_group.index, height=difficulty_group['y'],\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Difficulty\")\n",
    "lns.append(lns1)\n",
    "\n",
    "difficulty_group = difficulty_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(difficulty_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(difficulty_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_7f86c_row0_col0, #T_7f86c_row0_col1, #T_7f86c_row0_col2, #T_7f86c_row0_col3, #T_7f86c_row0_col4, #T_7f86c_row0_col5, #T_7f86c_row0_col6, #T_7f86c_row0_col7, #T_7f86c_row1_col0, #T_7f86c_row1_col1, #T_7f86c_row1_col2, #T_7f86c_row1_col3, #T_7f86c_row1_col4, #T_7f86c_row1_col5, #T_7f86c_row1_col6, #T_7f86c_row1_col7, #T_7f86c_row2_col0, #T_7f86c_row2_col1, #T_7f86c_row2_col2, #T_7f86c_row2_col3, #T_7f86c_row2_col4, #T_7f86c_row2_col5, #T_7f86c_row2_col6, #T_7f86c_row2_col7, #T_7f86c_row3_col0, #T_7f86c_row3_col1, #T_7f86c_row3_col2, #T_7f86c_row3_col3, #T_7f86c_row3_col4, #T_7f86c_row3_col6, #T_7f86c_row4_col0, #T_7f86c_row4_col1, #T_7f86c_row4_col2, #T_7f86c_row4_col3, #T_7f86c_row4_col4, #T_7f86c_row4_col5, #T_7f86c_row5_col0, #T_7f86c_row5_col1, #T_7f86c_row5_col2, #T_7f86c_row5_col3, #T_7f86c_row6_col0, #T_7f86c_row6_col1, #T_7f86c_row6_col2, #T_7f86c_row7_col0, #T_7f86c_row7_col1, #T_7f86c_row7_col2, #T_7f86c_row8_col0, #T_7f86c_row8_col1, #T_7f86c_row9_col0, #T_7f86c_row10_col0, #T_7f86c_row14_col8, #T_7f86c_row15_col7, #T_7f86c_row15_col8, #T_7f86c_row16_col5, #T_7f86c_row16_col6, #T_7f86c_row16_col7, #T_7f86c_row16_col8, #T_7f86c_row17_col4, #T_7f86c_row17_col5, #T_7f86c_row17_col6, #T_7f86c_row17_col7, #T_7f86c_row17_col8, #T_7f86c_row18_col1, #T_7f86c_row18_col3, #T_7f86c_row18_col4, #T_7f86c_row18_col5, #T_7f86c_row18_col6, #T_7f86c_row18_col7, #T_7f86c_row18_col8 {\n",
       "  background-color: #000000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7f86c_row0_col8, #T_7f86c_row5_col7, #T_7f86c_row8_col3, #T_7f86c_row17_col3 {\n",
       "  background-color: #e9e9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row1_col8 {\n",
       "  background-color: #ff7d7d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7f86c_row2_col8, #T_7f86c_row11_col7 {\n",
       "  background-color: #ffd9d9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row3_col5, #T_7f86c_row14_col6, #T_7f86c_row16_col3 {\n",
       "  background-color: #ffa1a1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row3_col7, #T_7f86c_row7_col5 {\n",
       "  background-color: #a5a5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row3_col8, #T_7f86c_row5_col5, #T_7f86c_row10_col3 {\n",
       "  background-color: #fff9f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row4_col6 {\n",
       "  background-color: #ff4d4d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7f86c_row4_col7, #T_7f86c_row8_col5, #T_7f86c_row15_col1, #T_7f86c_row17_col2 {\n",
       "  background-color: #c1c1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row4_col8, #T_7f86c_row7_col7, #T_7f86c_row9_col6 {\n",
       "  background-color: #f9f9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row5_col4, #T_7f86c_row7_col6, #T_7f86c_row10_col7, #T_7f86c_row11_col4 {\n",
       "  background-color: #ffc9c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row5_col6, #T_7f86c_row8_col7, #T_7f86c_row9_col4, #T_7f86c_row9_col5, #T_7f86c_row15_col2 {\n",
       "  background-color: #f1f1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row5_col8, #T_7f86c_row10_col5, #T_7f86c_row11_col2, #T_7f86c_row13_col2 {\n",
       "  background-color: #f5f5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row6_col3, #T_7f86c_row10_col6, #T_7f86c_row13_col3 {\n",
       "  background-color: #ffd1d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row6_col4, #T_7f86c_row6_col5, #T_7f86c_row8_col4 {\n",
       "  background-color: #fdfdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row6_col6, #T_7f86c_row8_col6, #T_7f86c_row10_col4, #T_7f86c_row11_col3 {\n",
       "  background-color: #fff1f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row6_col7 {\n",
       "  background-color: #d1d1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row6_col8, #T_7f86c_row7_col3, #T_7f86c_row9_col3, #T_7f86c_row14_col4 {\n",
       "  background-color: #ffe9e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row7_col4, #T_7f86c_row13_col4, #T_7f86c_row13_col5, #T_7f86c_row15_col4, #T_7f86c_row16_col4 {\n",
       "  background-color: #ffdddd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row7_col8, #T_7f86c_row9_col8 {\n",
       "  background-color: #ffb5b5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row8_col2, #T_7f86c_row12_col3 {\n",
       "  background-color: #ffe5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row8_col8, #T_7f86c_row14_col7 {\n",
       "  background-color: #ffa5a5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row9_col1, #T_7f86c_row14_col2 {\n",
       "  background-color: #ededff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row9_col2, #T_7f86c_row16_col2 {\n",
       "  background-color: #e1e1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row9_col7, #T_7f86c_row11_col8, #T_7f86c_row13_col7 {\n",
       "  background-color: #ffe1e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row10_col1 {\n",
       "  background-color: #ddddff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row10_col2 {\n",
       "  background-color: #c5c5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row10_col8, #T_7f86c_row11_col6, #T_7f86c_row15_col3 {\n",
       "  background-color: #ffbdbd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row11_col0 {\n",
       "  background-color: #9191ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7f86c_row11_col1, #T_7f86c_row13_col1, #T_7f86c_row14_col1, #T_7f86c_row16_col1 {\n",
       "  background-color: #b5b5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row11_col5 {\n",
       "  background-color: #c9c9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row12_col0 {\n",
       "  background-color: #7d7dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7f86c_row12_col1 {\n",
       "  background-color: #b9b9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row12_col2 {\n",
       "  background-color: #d9d9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row12_col4 {\n",
       "  background-color: #ff9d9d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row12_col5, #T_7f86c_row15_col5 {\n",
       "  background-color: #ffcdcd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row12_col6 {\n",
       "  background-color: #ff9595;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row12_col7 {\n",
       "  background-color: #ffeded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row12_col8 {\n",
       "  background-color: #ffc1c1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row13_col0 {\n",
       "  background-color: #7979ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7f86c_row13_col6 {\n",
       "  background-color: #ff8d8d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row13_col8 {\n",
       "  background-color: #ffa9a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row14_col0 {\n",
       "  background-color: #9d9dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7f86c_row14_col3 {\n",
       "  background-color: #ffb9b9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row14_col5 {\n",
       "  background-color: #ffb1b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row15_col0 {\n",
       "  background-color: #8585ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7f86c_row15_col6 {\n",
       "  background-color: #ff9191;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7f86c_row16_col0 {\n",
       "  background-color: #6161ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7f86c_row17_col0 {\n",
       "  background-color: #2d2dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7f86c_row17_col1 {\n",
       "  background-color: #5959ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7f86c_row18_col0 {\n",
       "  background-color: #1515ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7f86c_row18_col2 {\n",
       "  background-color: #cdcdff;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7f86c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >d_bin</th>\n",
       "      <th id=\"T_7f86c_level0_col0\" class=\"col_heading level0 col0\" >1</th>\n",
       "      <th id=\"T_7f86c_level0_col1\" class=\"col_heading level0 col1\" >2</th>\n",
       "      <th id=\"T_7f86c_level0_col2\" class=\"col_heading level0 col2\" >3</th>\n",
       "      <th id=\"T_7f86c_level0_col3\" class=\"col_heading level0 col3\" >5</th>\n",
       "      <th id=\"T_7f86c_level0_col4\" class=\"col_heading level0 col4\" >6</th>\n",
       "      <th id=\"T_7f86c_level0_col5\" class=\"col_heading level0 col5\" >7</th>\n",
       "      <th id=\"T_7f86c_level0_col6\" class=\"col_heading level0 col6\" >8</th>\n",
       "      <th id=\"T_7f86c_level0_col7\" class=\"col_heading level0 col7\" >9</th>\n",
       "      <th id=\"T_7f86c_level0_col8\" class=\"col_heading level0 col8\" >10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >s_bin</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row0\" class=\"row_heading level0 row0\" >0.360000</th>\n",
       "      <td id=\"T_7f86c_row0_col0\" class=\"data row0 col0\" ></td>\n",
       "      <td id=\"T_7f86c_row0_col1\" class=\"data row0 col1\" ></td>\n",
       "      <td id=\"T_7f86c_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "      <td id=\"T_7f86c_row0_col3\" class=\"data row0 col3\" ></td>\n",
       "      <td id=\"T_7f86c_row0_col4\" class=\"data row0 col4\" ></td>\n",
       "      <td id=\"T_7f86c_row0_col5\" class=\"data row0 col5\" ></td>\n",
       "      <td id=\"T_7f86c_row0_col6\" class=\"data row0 col6\" ></td>\n",
       "      <td id=\"T_7f86c_row0_col7\" class=\"data row0 col7\" ></td>\n",
       "      <td id=\"T_7f86c_row0_col8\" class=\"data row0 col8\" >-0.89%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row1\" class=\"row_heading level0 row1\" >0.510000</th>\n",
       "      <td id=\"T_7f86c_row1_col0\" class=\"data row1 col0\" ></td>\n",
       "      <td id=\"T_7f86c_row1_col1\" class=\"data row1 col1\" ></td>\n",
       "      <td id=\"T_7f86c_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "      <td id=\"T_7f86c_row1_col3\" class=\"data row1 col3\" ></td>\n",
       "      <td id=\"T_7f86c_row1_col4\" class=\"data row1 col4\" ></td>\n",
       "      <td id=\"T_7f86c_row1_col5\" class=\"data row1 col5\" ></td>\n",
       "      <td id=\"T_7f86c_row1_col6\" class=\"data row1 col6\" ></td>\n",
       "      <td id=\"T_7f86c_row1_col7\" class=\"data row1 col7\" ></td>\n",
       "      <td id=\"T_7f86c_row1_col8\" class=\"data row1 col8\" >5.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row2\" class=\"row_heading level0 row2\" >0.710000</th>\n",
       "      <td id=\"T_7f86c_row2_col0\" class=\"data row2 col0\" ></td>\n",
       "      <td id=\"T_7f86c_row2_col1\" class=\"data row2 col1\" ></td>\n",
       "      <td id=\"T_7f86c_row2_col2\" class=\"data row2 col2\" ></td>\n",
       "      <td id=\"T_7f86c_row2_col3\" class=\"data row2 col3\" ></td>\n",
       "      <td id=\"T_7f86c_row2_col4\" class=\"data row2 col4\" ></td>\n",
       "      <td id=\"T_7f86c_row2_col5\" class=\"data row2 col5\" ></td>\n",
       "      <td id=\"T_7f86c_row2_col6\" class=\"data row2 col6\" ></td>\n",
       "      <td id=\"T_7f86c_row2_col7\" class=\"data row2 col7\" ></td>\n",
       "      <td id=\"T_7f86c_row2_col8\" class=\"data row2 col8\" >1.43%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row3\" class=\"row_heading level0 row3\" >1.000000</th>\n",
       "      <td id=\"T_7f86c_row3_col0\" class=\"data row3 col0\" ></td>\n",
       "      <td id=\"T_7f86c_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_7f86c_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "      <td id=\"T_7f86c_row3_col3\" class=\"data row3 col3\" ></td>\n",
       "      <td id=\"T_7f86c_row3_col4\" class=\"data row3 col4\" ></td>\n",
       "      <td id=\"T_7f86c_row3_col5\" class=\"data row3 col5\" >3.66%</td>\n",
       "      <td id=\"T_7f86c_row3_col6\" class=\"data row3 col6\" ></td>\n",
       "      <td id=\"T_7f86c_row3_col7\" class=\"data row3 col7\" >-3.54%</td>\n",
       "      <td id=\"T_7f86c_row3_col8\" class=\"data row3 col8\" >0.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row4\" class=\"row_heading level0 row4\" >1.400000</th>\n",
       "      <td id=\"T_7f86c_row4_col0\" class=\"data row4 col0\" ></td>\n",
       "      <td id=\"T_7f86c_row4_col1\" class=\"data row4 col1\" ></td>\n",
       "      <td id=\"T_7f86c_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "      <td id=\"T_7f86c_row4_col3\" class=\"data row4 col3\" ></td>\n",
       "      <td id=\"T_7f86c_row4_col4\" class=\"data row4 col4\" ></td>\n",
       "      <td id=\"T_7f86c_row4_col5\" class=\"data row4 col5\" ></td>\n",
       "      <td id=\"T_7f86c_row4_col6\" class=\"data row4 col6\" >6.92%</td>\n",
       "      <td id=\"T_7f86c_row4_col7\" class=\"data row4 col7\" >-2.46%</td>\n",
       "      <td id=\"T_7f86c_row4_col8\" class=\"data row4 col8\" >-0.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row5\" class=\"row_heading level0 row5\" >1.960000</th>\n",
       "      <td id=\"T_7f86c_row5_col0\" class=\"data row5 col0\" ></td>\n",
       "      <td id=\"T_7f86c_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_7f86c_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_7f86c_row5_col3\" class=\"data row5 col3\" ></td>\n",
       "      <td id=\"T_7f86c_row5_col4\" class=\"data row5 col4\" >2.15%</td>\n",
       "      <td id=\"T_7f86c_row5_col5\" class=\"data row5 col5\" >0.17%</td>\n",
       "      <td id=\"T_7f86c_row5_col6\" class=\"data row5 col6\" >-0.55%</td>\n",
       "      <td id=\"T_7f86c_row5_col7\" class=\"data row5 col7\" >-0.89%</td>\n",
       "      <td id=\"T_7f86c_row5_col8\" class=\"data row5 col8\" >-0.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row6\" class=\"row_heading level0 row6\" >2.740000</th>\n",
       "      <td id=\"T_7f86c_row6_col0\" class=\"data row6 col0\" ></td>\n",
       "      <td id=\"T_7f86c_row6_col1\" class=\"data row6 col1\" ></td>\n",
       "      <td id=\"T_7f86c_row6_col2\" class=\"data row6 col2\" ></td>\n",
       "      <td id=\"T_7f86c_row6_col3\" class=\"data row6 col3\" >1.73%</td>\n",
       "      <td id=\"T_7f86c_row6_col4\" class=\"data row6 col4\" >-0.05%</td>\n",
       "      <td id=\"T_7f86c_row6_col5\" class=\"data row6 col5\" >-0.10%</td>\n",
       "      <td id=\"T_7f86c_row6_col6\" class=\"data row6 col6\" >0.56%</td>\n",
       "      <td id=\"T_7f86c_row6_col7\" class=\"data row6 col7\" >-1.73%</td>\n",
       "      <td id=\"T_7f86c_row6_col8\" class=\"data row6 col8\" >0.79%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row7\" class=\"row_heading level0 row7\" >3.840000</th>\n",
       "      <td id=\"T_7f86c_row7_col0\" class=\"data row7 col0\" ></td>\n",
       "      <td id=\"T_7f86c_row7_col1\" class=\"data row7 col1\" ></td>\n",
       "      <td id=\"T_7f86c_row7_col2\" class=\"data row7 col2\" ></td>\n",
       "      <td id=\"T_7f86c_row7_col3\" class=\"data row7 col3\" >0.87%</td>\n",
       "      <td id=\"T_7f86c_row7_col4\" class=\"data row7 col4\" >1.28%</td>\n",
       "      <td id=\"T_7f86c_row7_col5\" class=\"data row7 col5\" >-3.47%</td>\n",
       "      <td id=\"T_7f86c_row7_col6\" class=\"data row7 col6\" >2.17%</td>\n",
       "      <td id=\"T_7f86c_row7_col7\" class=\"data row7 col7\" >-0.23%</td>\n",
       "      <td id=\"T_7f86c_row7_col8\" class=\"data row7 col8\" >2.85%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row8\" class=\"row_heading level0 row8\" >5.380000</th>\n",
       "      <td id=\"T_7f86c_row8_col0\" class=\"data row8 col0\" ></td>\n",
       "      <td id=\"T_7f86c_row8_col1\" class=\"data row8 col1\" ></td>\n",
       "      <td id=\"T_7f86c_row8_col2\" class=\"data row8 col2\" >0.95%</td>\n",
       "      <td id=\"T_7f86c_row8_col3\" class=\"data row8 col3\" >-0.91%</td>\n",
       "      <td id=\"T_7f86c_row8_col4\" class=\"data row8 col4\" >-0.08%</td>\n",
       "      <td id=\"T_7f86c_row8_col5\" class=\"data row8 col5\" >-2.35%</td>\n",
       "      <td id=\"T_7f86c_row8_col6\" class=\"data row8 col6\" >0.62%</td>\n",
       "      <td id=\"T_7f86c_row8_col7\" class=\"data row8 col7\" >-0.49%</td>\n",
       "      <td id=\"T_7f86c_row8_col8\" class=\"data row8 col8\" >3.59%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row9\" class=\"row_heading level0 row9\" >7.530000</th>\n",
       "      <td id=\"T_7f86c_row9_col0\" class=\"data row9 col0\" ></td>\n",
       "      <td id=\"T_7f86c_row9_col1\" class=\"data row9 col1\" >-0.70%</td>\n",
       "      <td id=\"T_7f86c_row9_col2\" class=\"data row9 col2\" >-1.20%</td>\n",
       "      <td id=\"T_7f86c_row9_col3\" class=\"data row9 col3\" >0.91%</td>\n",
       "      <td id=\"T_7f86c_row9_col4\" class=\"data row9 col4\" >-0.53%</td>\n",
       "      <td id=\"T_7f86c_row9_col5\" class=\"data row9 col5\" >-0.52%</td>\n",
       "      <td id=\"T_7f86c_row9_col6\" class=\"data row9 col6\" >-0.24%</td>\n",
       "      <td id=\"T_7f86c_row9_col7\" class=\"data row9 col7\" >1.15%</td>\n",
       "      <td id=\"T_7f86c_row9_col8\" class=\"data row9 col8\" >2.95%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row10\" class=\"row_heading level0 row10\" >10.540000</th>\n",
       "      <td id=\"T_7f86c_row10_col0\" class=\"data row10 col0\" ></td>\n",
       "      <td id=\"T_7f86c_row10_col1\" class=\"data row10 col1\" >-1.37%</td>\n",
       "      <td id=\"T_7f86c_row10_col2\" class=\"data row10 col2\" >-2.30%</td>\n",
       "      <td id=\"T_7f86c_row10_col3\" class=\"data row10 col3\" >0.23%</td>\n",
       "      <td id=\"T_7f86c_row10_col4\" class=\"data row10 col4\" >0.54%</td>\n",
       "      <td id=\"T_7f86c_row10_col5\" class=\"data row10 col5\" >-0.39%</td>\n",
       "      <td id=\"T_7f86c_row10_col6\" class=\"data row10 col6\" >1.86%</td>\n",
       "      <td id=\"T_7f86c_row10_col7\" class=\"data row10 col7\" >2.17%</td>\n",
       "      <td id=\"T_7f86c_row10_col8\" class=\"data row10 col8\" >2.58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row11\" class=\"row_heading level0 row11\" >14.760000</th>\n",
       "      <td id=\"T_7f86c_row11_col0\" class=\"data row11 col0\" >-4.37%</td>\n",
       "      <td id=\"T_7f86c_row11_col1\" class=\"data row11 col1\" >-2.85%</td>\n",
       "      <td id=\"T_7f86c_row11_col2\" class=\"data row11 col2\" >-0.36%</td>\n",
       "      <td id=\"T_7f86c_row11_col3\" class=\"data row11 col3\" >0.60%</td>\n",
       "      <td id=\"T_7f86c_row11_col4\" class=\"data row11 col4\" >2.16%</td>\n",
       "      <td id=\"T_7f86c_row11_col5\" class=\"data row11 col5\" >-2.08%</td>\n",
       "      <td id=\"T_7f86c_row11_col6\" class=\"data row11 col6\" >2.53%</td>\n",
       "      <td id=\"T_7f86c_row11_col7\" class=\"data row11 col7\" >1.42%</td>\n",
       "      <td id=\"T_7f86c_row11_col8\" class=\"data row11 col8\" >1.11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row12\" class=\"row_heading level0 row12\" >20.660000</th>\n",
       "      <td id=\"T_7f86c_row12_col0\" class=\"data row12 col0\" >-5.05%</td>\n",
       "      <td id=\"T_7f86c_row12_col1\" class=\"data row12 col1\" >-2.78%</td>\n",
       "      <td id=\"T_7f86c_row12_col2\" class=\"data row12 col2\" >-1.52%</td>\n",
       "      <td id=\"T_7f86c_row12_col3\" class=\"data row12 col3\" >1.08%</td>\n",
       "      <td id=\"T_7f86c_row12_col4\" class=\"data row12 col4\" >3.85%</td>\n",
       "      <td id=\"T_7f86c_row12_col5\" class=\"data row12 col5\" >1.94%</td>\n",
       "      <td id=\"T_7f86c_row12_col6\" class=\"data row12 col6\" >4.10%</td>\n",
       "      <td id=\"T_7f86c_row12_col7\" class=\"data row12 col7\" >0.76%</td>\n",
       "      <td id=\"T_7f86c_row12_col8\" class=\"data row12 col8\" >2.38%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row13\" class=\"row_heading level0 row13\" >28.930000</th>\n",
       "      <td id=\"T_7f86c_row13_col0\" class=\"data row13 col0\" >-5.26%</td>\n",
       "      <td id=\"T_7f86c_row13_col1\" class=\"data row13 col1\" >-2.96%</td>\n",
       "      <td id=\"T_7f86c_row13_col2\" class=\"data row13 col2\" >-0.42%</td>\n",
       "      <td id=\"T_7f86c_row13_col3\" class=\"data row13 col3\" >1.84%</td>\n",
       "      <td id=\"T_7f86c_row13_col4\" class=\"data row13 col4\" >1.40%</td>\n",
       "      <td id=\"T_7f86c_row13_col5\" class=\"data row13 col5\" >1.28%</td>\n",
       "      <td id=\"T_7f86c_row13_col6\" class=\"data row13 col6\" >4.45%</td>\n",
       "      <td id=\"T_7f86c_row13_col7\" class=\"data row13 col7\" >1.16%</td>\n",
       "      <td id=\"T_7f86c_row13_col8\" class=\"data row13 col8\" >3.36%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row14\" class=\"row_heading level0 row14\" >40.500000</th>\n",
       "      <td id=\"T_7f86c_row14_col0\" class=\"data row14 col0\" >-3.79%</td>\n",
       "      <td id=\"T_7f86c_row14_col1\" class=\"data row14 col1\" >-2.88%</td>\n",
       "      <td id=\"T_7f86c_row14_col2\" class=\"data row14 col2\" >-0.69%</td>\n",
       "      <td id=\"T_7f86c_row14_col3\" class=\"data row14 col3\" >2.71%</td>\n",
       "      <td id=\"T_7f86c_row14_col4\" class=\"data row14 col4\" >0.89%</td>\n",
       "      <td id=\"T_7f86c_row14_col5\" class=\"data row14 col5\" >2.99%</td>\n",
       "      <td id=\"T_7f86c_row14_col6\" class=\"data row14 col6\" >3.67%</td>\n",
       "      <td id=\"T_7f86c_row14_col7\" class=\"data row14 col7\" >3.50%</td>\n",
       "      <td id=\"T_7f86c_row14_col8\" class=\"data row14 col8\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row15\" class=\"row_heading level0 row15\" >56.690000</th>\n",
       "      <td id=\"T_7f86c_row15_col0\" class=\"data row15 col0\" >-4.80%</td>\n",
       "      <td id=\"T_7f86c_row15_col1\" class=\"data row15 col1\" >-2.46%</td>\n",
       "      <td id=\"T_7f86c_row15_col2\" class=\"data row15 col2\" >-0.50%</td>\n",
       "      <td id=\"T_7f86c_row15_col3\" class=\"data row15 col3\" >2.53%</td>\n",
       "      <td id=\"T_7f86c_row15_col4\" class=\"data row15 col4\" >1.38%</td>\n",
       "      <td id=\"T_7f86c_row15_col5\" class=\"data row15 col5\" >1.90%</td>\n",
       "      <td id=\"T_7f86c_row15_col6\" class=\"data row15 col6\" >4.32%</td>\n",
       "      <td id=\"T_7f86c_row15_col7\" class=\"data row15 col7\" ></td>\n",
       "      <td id=\"T_7f86c_row15_col8\" class=\"data row15 col8\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row16\" class=\"row_heading level0 row16\" >79.370000</th>\n",
       "      <td id=\"T_7f86c_row16_col0\" class=\"data row16 col0\" >-6.11%</td>\n",
       "      <td id=\"T_7f86c_row16_col1\" class=\"data row16 col1\" >-2.96%</td>\n",
       "      <td id=\"T_7f86c_row16_col2\" class=\"data row16 col2\" >-1.11%</td>\n",
       "      <td id=\"T_7f86c_row16_col3\" class=\"data row16 col3\" >3.71%</td>\n",
       "      <td id=\"T_7f86c_row16_col4\" class=\"data row16 col4\" >1.38%</td>\n",
       "      <td id=\"T_7f86c_row16_col5\" class=\"data row16 col5\" ></td>\n",
       "      <td id=\"T_7f86c_row16_col6\" class=\"data row16 col6\" ></td>\n",
       "      <td id=\"T_7f86c_row16_col7\" class=\"data row16 col7\" ></td>\n",
       "      <td id=\"T_7f86c_row16_col8\" class=\"data row16 col8\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row17\" class=\"row_heading level0 row17\" >111.120000</th>\n",
       "      <td id=\"T_7f86c_row17_col0\" class=\"data row17 col0\" >-8.20%</td>\n",
       "      <td id=\"T_7f86c_row17_col1\" class=\"data row17 col1\" >-6.43%</td>\n",
       "      <td id=\"T_7f86c_row17_col2\" class=\"data row17 col2\" >-2.49%</td>\n",
       "      <td id=\"T_7f86c_row17_col3\" class=\"data row17 col3\" >-0.82%</td>\n",
       "      <td id=\"T_7f86c_row17_col4\" class=\"data row17 col4\" ></td>\n",
       "      <td id=\"T_7f86c_row17_col5\" class=\"data row17 col5\" ></td>\n",
       "      <td id=\"T_7f86c_row17_col6\" class=\"data row17 col6\" ></td>\n",
       "      <td id=\"T_7f86c_row17_col7\" class=\"data row17 col7\" ></td>\n",
       "      <td id=\"T_7f86c_row17_col8\" class=\"data row17 col8\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7f86c_level0_row18\" class=\"row_heading level0 row18\" >155.570000</th>\n",
       "      <td id=\"T_7f86c_row18_col0\" class=\"data row18 col0\" >-9.14%</td>\n",
       "      <td id=\"T_7f86c_row18_col1\" class=\"data row18 col1\" ></td>\n",
       "      <td id=\"T_7f86c_row18_col2\" class=\"data row18 col2\" >-1.88%</td>\n",
       "      <td id=\"T_7f86c_row18_col3\" class=\"data row18 col3\" ></td>\n",
       "      <td id=\"T_7f86c_row18_col4\" class=\"data row18 col4\" ></td>\n",
       "      <td id=\"T_7f86c_row18_col5\" class=\"data row18 col5\" ></td>\n",
       "      <td id=\"T_7f86c_row18_col6\" class=\"data row18 col6\" ></td>\n",
       "      <td id=\"T_7f86c_row18_col7\" class=\"data row18 col7\" ></td>\n",
       "      <td id=\"T_7f86c_row18_col8\" class=\"data row18 col8\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b10ad340>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw = dataset[['difficulty', 'stability', 'p', 'y']].copy()\n",
    "B_W_Metric_raw['s_bin'] = B_W_Metric_raw['stability'].map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2))\n",
    "B_W_Metric_raw['d_bin'] = B_W_Metric_raw['difficulty'].map(lambda x: int(round(x)))\n",
    "B_W_Metric = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('mean').reset_index()\n",
    "B_W_Metric_count = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('count').reset_index()\n",
    "B_W_Metric['B-W'] = B_W_Metric['p'] - B_W_Metric['y']\n",
    "n = len(dataset)\n",
    "bins = len(B_W_Metric)\n",
    "B_W_Metric_pivot = B_W_Metric[B_W_Metric_count['p'] > max(50, n / (3 * bins))].pivot(index=\"s_bin\", columns='d_bin', values='B-W')\n",
    "B_W_Metric_pivot.apply(pd.to_numeric).style.background_gradient(cmap='seismic', axis=None, vmin=-0.2, vmax=0.2).format(\"{:.2%}\", na_rep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DiffMax: 0.13272825792468168\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S_rounded</th>\n",
       "      <th>D_rounded</th>\n",
       "      <th>R_t_rounded</th>\n",
       "      <th>R_measured</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.51</td>\n",
       "      <td>10</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.778903</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.71</td>\n",
       "      <td>10</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.871560</td>\n",
       "      <td>1199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.71</td>\n",
       "      <td>10</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.878146</td>\n",
       "      <td>755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1.00</td>\n",
       "      <td>7</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.734024</td>\n",
       "      <td>1158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1.00</td>\n",
       "      <td>7</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.740522</td>\n",
       "      <td>1187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>79.37</td>\n",
       "      <td>1</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.961783</td>\n",
       "      <td>942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1669</th>\n",
       "      <td>79.37</td>\n",
       "      <td>2</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.931148</td>\n",
       "      <td>610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>79.37</td>\n",
       "      <td>3</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.898263</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>111.12</td>\n",
       "      <td>1</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.954802</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>155.57</td>\n",
       "      <td>3</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.944290</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      S_rounded  D_rounded  R_t_rounded  R_measured  count\n",
       "36         0.51         10        0.850    0.778903   1185\n",
       "57         0.71         10        0.875    0.871560   1199\n",
       "58         0.71         10        0.900    0.878146    755\n",
       "74         1.00          7        0.625    0.734024   1158\n",
       "75         1.00          7        0.700    0.740522   1187\n",
       "...         ...        ...          ...         ...    ...\n",
       "1656      79.37          1        0.900    0.961783    942\n",
       "1669      79.37          2        0.900    0.931148    610\n",
       "1685      79.37          3        0.950    0.898263    403\n",
       "1779     111.12          1        0.875    0.954802    354\n",
       "1906     155.57          3        0.950    0.944290    359\n",
       "\n",
       "[177 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw['r_bin'] = B_W_Metric_raw['p'].map(lambda x: round(x * 4, 1) / 4)\n",
    "R_Matrix = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin', 'r_bin']).agg({'y': ['mean', 'count']}).reset_index()\n",
    "R_Matrix.columns = ['S_rounded', 'D_rounded', 'R_t_rounded', 'R_measured', 'count']\n",
    "R_Matrix = R_Matrix[R_Matrix['count'] > 300]\n",
    "print(\"DiffMax:\", max(abs(R_Matrix['R_t_rounded'] - R_Matrix['R_measured'])))\n",
    "R_Matrix"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Comparision with Anki bulit-in algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def anki(history):\n",
    "    ivl = 0\n",
    "    start_ease = 2.5\n",
    "    hard_interval = 1.2\n",
    "    ease_bonus = 1.3\n",
    "    graduating_interval = 1\n",
    "    easy_interval = 4\n",
    "    new_interval = 0\n",
    "    minimum_interval = 1\n",
    "    interval_modifier = 1\n",
    "    for i, (delta_t, rating) in enumerate(history):\n",
    "        delta_t = delta_t.item()\n",
    "        rating = rating.item()\n",
    "        if i == 0:\n",
    "            ease = start_ease\n",
    "            if rating == 4:\n",
    "                ivl = easy_interval\n",
    "            else:\n",
    "                ivl = graduating_interval\n",
    "        else:\n",
    "            delay = delta_t - ivl\n",
    "            if delay >= 0:\n",
    "                if rating == 1:\n",
    "                    ivl = max(ivl * new_interval, minimum_interval)\n",
    "                    ease -= 0.2\n",
    "                elif rating == 2:\n",
    "                    ivl = ivl * hard_interval\n",
    "                    ease -= 0.15\n",
    "                elif rating == 3:\n",
    "                    ivl = (ivl + delay // 2) * ease\n",
    "                else:\n",
    "                    ivl = (ivl + delay) * ease * ease_bonus\n",
    "                    ease += 0.15\n",
    "            # early_review\n",
    "            else:\n",
    "                if rating == 1:\n",
    "                    ivl = max(ivl * new_interval, minimum_interval)\n",
    "                    ease -= 0.2\n",
    "                elif rating == 2:\n",
    "                    ivl = max(delta_t * hard_interval / 2, ivl * hard_interval)\n",
    "                    ease -= 0.15\n",
    "                elif rating == 3:\n",
    "                    ivl = max(delta_t * ease, ivl)\n",
    "                else:\n",
    "                    ivl = max(delta_t * ease, ivl) * (0.5 * ease_bonus + 0.5)\n",
    "                    ease += 0.15\n",
    "            ivl = ivl * interval_modifier\n",
    "        ease = max(1.3, ease)\n",
    "        ivl = max(1, round(ivl+0.01))\n",
    "    return ivl\n",
    "\n",
    "def sm2(history):\n",
    "    ivl = 0\n",
    "    ef = 2.5\n",
    "    reps = 0\n",
    "    for delta_t, rating in history:\n",
    "        delta_t = delta_t.item()\n",
    "        rating = rating.item() + 1\n",
    "        if rating > 2:\n",
    "            if reps == 0:\n",
    "                ivl = 1\n",
    "                reps = 1\n",
    "            elif reps == 1:\n",
    "                ivl = 6\n",
    "                reps = 2\n",
    "            else:\n",
    "                ivl = ivl * ef\n",
    "                reps += 1\n",
    "        else:\n",
    "            ivl = 1\n",
    "            reps = 0\n",
    "        ef = max(1.3, ef + (0.1 - (5 - rating) * (0.08 + (5 - rating) * 0.02)))\n",
    "        ivl = max(1, round(ivl+0.01))\n",
    "    return ivl\n",
    "\n",
    "dataset['sm2_interval'] = dataset['tensor'].map(sm2)\n",
    "dataset['sm2_p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['sm2_interval'])\n",
    "cross_comparison = dataset[['sm2_p', 'p', 'y']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_Metric:  0.03593267235353381\n",
      "R-squared: -21.9800\n",
      "RMSE: 0.1412\n",
      "[0.76541153 0.15733363]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R_Metric = mean_squared_error(cross_comparison['y'], cross_comparison['sm2_p'], squared=False) - mean_squared_error(cross_comparison['y'], cross_comparison['p'], squared=False)\n",
    "print(\"R_Metric: \", R_Metric)\n",
    "plot_brier(dataset['sm2_p'], dataset['y'], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Universal Metric of FSRS: 0.0137\n",
      "Universal Metric of SM2: 0.0670\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "cross_comparison['SM2_B-W'] = cross_comparison['sm2_p'] - cross_comparison['y']\n",
    "cross_comparison['SM2_bin'] = cross_comparison['sm2_p'].map(lambda x: round(x, 1))\n",
    "cross_comparison['FSRS_B-W'] = cross_comparison['p'] - cross_comparison['y']\n",
    "cross_comparison['FSRS_bin'] = cross_comparison['p'].map(lambda x: round(x, 1))\n",
    "\n",
    "plt.axhline(y = 0.0, color = 'black', linestyle = '-')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='SM2_bin').agg({'y': ['mean'], 'FSRS_B-W': ['mean'], 'p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of FSRS: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['p', 'mean'], sample_weight=cross_comparison_group['p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['p', 'percent'] = cross_comparison_group['p', 'count'] / cross_comparison_group['p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['FSRS_B-W', 'mean'], s=cross_comparison_group['p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['FSRS_B-W', 'mean'], label='FSRS by SM2')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='FSRS_bin').agg({'y': ['mean'], 'SM2_B-W': ['mean'], 'sm2_p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of SM2: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['sm2_p', 'mean'], sample_weight=cross_comparison_group['sm2_p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['sm2_p', 'percent'] = cross_comparison_group['sm2_p', 'count'] / cross_comparison_group['sm2_p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['SM2_B-W', 'mean'], s=cross_comparison_group['sm2_p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['SM2_B-W', 'mean'], label='SM2 by FSRS')\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.grid(linestyle='--')\n",
    "plt.title(\"SM2 vs. FSRS\")\n",
    "plt.xlabel('Predicted R')\n",
    "plt.ylabel('B-W Metric')\n",
    "plt.xlim(0, 1)\n",
    "plt.xticks(np.arange(0, 1.1, 0.1))\n",
    "plt.show()"
   ]
  }
 ],
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